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2005 | Buch

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005

8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I

herausgegeben von: James S. Duncan, Guido Gerig

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

insite
SUCHEN

Inhaltsverzeichnis

Frontmatter

Image Analysis and Validation

Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.

Yong Fan, Dinggang Shen, Christos Davatzikos
Bone Enhancement Filtering: Application to Sinus Bone Segmentation and Simulation of Pituitary Surgery

We present a novel multi-scale bone enhancement measure that can be used to drive a geometric flow to segment bone structures. This measure has the essential properties to be incorporated in the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We present synthetic examples that validate our approach and show a comparison between existing segmentation techniques of paranasal sinus CT data.

Maxime Descoteaux, Michel Audette, Kiyoyuki Chinzei, Kaleem Siddiqi
Simultaneous Registration and Segmentation of Anatomical Structures from Brain MRI

In this paper, we present a novel variational formulation of the registration assisted image segmentation problem which leads to solving a coupled set of nonlinear PDEs that are solved using efficient numerical schemes. Our work is a departure from earlier methods in that we have a unified variational principle wherein non-rigid registration and segmentation are simultaneously achieved; unlike previous methods of solution for this problem, our algorithm can accommodate for image pairs having very distinct intensity distributions. We present examples of performance of our algorithm on synthetic and real data sets along with quantitative accuracy estimates of the registration.

Fei Wang, Baba C. Vemuri
Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation

Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We propose a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. We propose the use of warping and geodesic interpolation on the diffusion tensors to simulate the displacement and the destruction of the white matter fibers. We also model the process where the contrast agent tends to accumulate in cortical csf regions and active tumor regions to obtain contrast enhanced T1w MR image that appear realistic. The result is simulated multi-modal MRI with ground truth available as sets of probability maps. The system will be able to generate large sets of simulation images with tumors of varying size, shape and location, and will additionally generate infiltrated and deformed healthy tissue probabilities.

Marcel Prastawa, Elizabeth Bullitt, Guido Gerig

Vascular Image Segmentation

Automatic Cerebrovascular Segmentation by Accurate Probabilistic Modeling of TOF-MRA Images

Accurate automatic extraction of a 3D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to small size objects of interest (blood vessels) in each 2D MRA slice and complex surrounding anatomical structures, e.g. fat, bones, or grey and white brain matter. We show that due to a multi-modal nature of MRA data blood vessels can be accurately separated from background in each slice by a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, and we modify the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.

Ayman El-Baz, Aly A. Farag, Georgy Gimel’farb, Stephen G. Hushek
A Segmentation and Reconstruction Technique for 3D Vascular Structures

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

Vincent Luboz, Xunlei Wu, Karl Krissian, Carl-Fredrik Westin, Ron Kikinis, Stéphane Cotin, Steve Dawson
MRA Image Segmentation with Capillary Active Contour

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Our objective is to develop a specific segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm, called the capillary active contour (CAC), models capillary action where liquid can climb along the boundaries of thin tubes. The CAC, which is implemented based on level sets, is able to segment thin vessels and has been applied for verification on synthetic volumetric images and real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, our experiments show that the introduced capillary force can facilitate more accurate segmentation of blood vessels.

Pingkun Yan, Ashraf A. Kassim
Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination

Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points.

We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.

Stephen R. Aylward, Julien Jomier, Christelle Vivert, Vincent LeDigarcher, Elizabeth Bullitt

Image Registration I

Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis

The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected 3D objects and has been used in many applications in medical imaging. Previous SPHARM techniques use the first order ellipsoid for establishing surface correspondence and aligning objects. However, this first order information may not be sufficient in many cases; a more general method for establishing surface correspondence would be to minimize the mean squared distance between two corresponding surfaces. In this paper, a new surface matching algorithm is proposed for 3D SPHARM models to achieve this goal. This algorithm employs a useful rotational property of spherical harmonic basis functions for a fast implementation. Applications of medical image analysis (

e.g.

, spatio-temporal modeling of heart shape changes) are used to demonstrate this approach. Theoretical proofs and experimental results show that our approach is an accurate and flexible surface correspondence alignment method.

Heng Huang, Li Shen, Rong Zhang, Fillia Makedon, Bruce Hettleman, Justin Pearlman
Unified Point Selection and Surface-Based Registration Using a Particle Filter

We propose an algorithm for jointly performing registration point selection and interactive, rigid, surface-based registration. The registration is computed using a particle filter that outputs a sampled representation of the distribution of the registration parameters. The distribution is propagated through a point selection algorithm derived from a stiffness model of surface-based registration, allowing the selection algorithm to incorporate knowledge of the uncertainties in the registration parameters. We show that the behavior of target registration error improves as the quality measure of the registration points increases.

Burton Ma, Randy E. Ellis
Elastic Registration of 3D Ultrasound Images

3D registration of ultrasound images is an important and fast-growing research area with various medical applications, such as image-guided radiotherapy and surgery. However, this registration process is extremely challenging due to the deformation of soft tissue and the existence of speckles in these images. This paper presents a novel intra-modality elastic registration technique for 3D ultrasound images. It uses the general concept of attribute vectors to find the corresponding voxels in the fixed and moving images. The method does not require any pre-segmentation and does not employ any numerical optimization procedure. Therefore, the computational requirements are very low and it has the potential to be used for real-time applications. The technique is implemented and tested for 3D ultrasound images of liver, captured by a 3D ultrasound transducer. The results show that the method is sufficiently accurate and robust and is not easily trapped with local minima.

Pezhman Foroughi, Purang Abolmaesumi
Tracer Kinetic Model-Driven Registration for Dynamic Contrast Enhanced MRI Time Series

Motion during time-series data acquisition causes model-fitting errors in quantitative dynamic contrast-enhanced (DCE) MRI studies. Motion correction techniques using conventional registration cost functions may produce biased results because they were not designed to deal with the time-varying information content due to contrast enhancement. We present a locally-controlled, 3D translational registration process driven by tracer kinetic modeling that successfully registers abdominal DCE-MRI data at high temporal resolution and compare this method to a similar approach based on registration to the time series mean image in data from 8 patients. When the registration is driven by an appropriate model, we find significant improvements in model-fitting. Also, model-driven registration influences parameter estimates and reduces repeat study variability in measurements of blood volume.

Giovanni A. Buonaccorsi, Caleb Roberts, Sue Cheung, Yvonne Watson, Karen Davies, Alan Jackson, Gordon C. Jayson, Geoff J. M. Parker
Generalised Overlap Measures for Assessment of Pairwise and Groupwise Image Registration and Segmentation

Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.

William R. Crum, Oscar Camara, Daniel Rueckert, Kanwal K. Bhatia, Mark Jenkinson, Derek L. G. Hill

Diffusion Tensor Image Analysis

Uncertainty in White Matter Fiber Tractography

In this work we address the uncertainty associated with fiber paths obtained in white matter fiber tractography. This uncertainty, which arises for example from noise and partial volume effects, is quantified using a Bayesian modeling framework. The theory for estimating the probability of a connection between two areas in the brain is presented, and a new model of the local water diffusion profile is introduced. We also provide a theorem that facilitates the estimation of the parameters in this diffusion model, making the presented method simple to implement.

Ola Friman, Carl-Fredrik Westin
Fast and Simple Calculus on Tensors in the Log-Euclidean Framework

Computations on tensors have become common with the use of DT-MRI. But the classical Euclidean framework has many defects, and affine-invariant Riemannian metrics have been proposed to correct them. These metrics have excellent theoretical properties but lead to complex and slow algorithms. To remedy this limitation, we propose new metrics called Log-Euclidean. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. Indeed, Log-Euclidean computations are Euclidean computations in the domain of matrix logarithms. Theoretical aspects are presented and experimental results for multilinear interpolation and regularization of tensor fields are shown on synthetic and real DTI data.

Vincent Arsigny, Pierre Fillard, Xavier Pennec, Nicholas Ayache
3D Curve Inference for Diffusion MRI Regularization

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker’s 2D curve inference approach [8] by using a notion of

co-helicity

to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired

in vivo

from a human brain.

Peter Savadjiev, Jennifer S. W. Campbell, G. Bruce Pike, Kaleem Siddiqi
Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis

Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.

Isabelle Corouge, P. Thomas Fletcher, Sarang Joshi, John H. Gilmore, Guido Gerig
White Matter Tract Clustering and Correspondence in Populations

We present a novel method for finding white matter fiber correspondences and clusters across a population of brains. Our input is a collection of paths from tractography in every brain. Using spectral methods we embed each path as a vector in a high dimensional space. We create the embedding space so that it is common across all brains, consequently similar paths in all brains will map to points near each other in the space. By performing clustering in this space we are able to find matching fiber tract clusters in all brains. In addition, we automatically obtain correspondence of tractographic paths across brains: by selecting one or several paths of interest in one brain, the most similar paths in all brains are obtained as the nearest points in the high-dimensional space.

Lauren O’Donnell, Carl-Fredrik Westin
76-Space Analysis of Grey Matter Diffusivity: Methods and Applications

Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are widely used in the study and diagnosis of neurological diseases involving the White Matter (WM). However, many neurological and neurodegenerative diseases (e.g., Alzheimer’s disease and Creutzfeldt-Jakob disease) are generally considered to involve the Grey Matter (GM). Investigation of GM diffusivity of normal aging and pathological brains has both scientific significance and clinical applications. Most of previous research reports on quantification of GM diffusivity were based on the manually labeled Region of Interests (ROI) analysis of specific neuroanatomic regions. The well-known drawbacks of ROI analysis include inter-rater variations, irreproducible results, tediousness, and requirement of a priori definition of interested regions. In this paper, we present a new framework of automated 76-space analysis of GM diffusivity using DWI/DTI. The framework will be evaluated using clinical data, and applied for study of normal brain, Creutzfeldt-Jakob disease and Schizophrenia.

Tianming Liu, Geoffrey Young, Ling Huang, Nan-Kuei Chen, Stephen TC Wong
Fast Orientation Mapping from HARDI

This paper introduces a new, accurate and fast method for fiber orientation mapping using high angular resolution diffusion imaging (HARDI) data. The approach utilizes the Fourier relationship between the water displacement probabilities and diffusion attenuated magnetic resonance (MR) signal expressed in spherical coordinates. The Laplace series coefficients of the water displacement probabilities are evaluated at a fixed distance away from the origin. The computations take under one minute for most three-dimensional datasets. We present orientation maps computed from excised rat optic chiasm, brain and spinal cord images. The developed method will improve the reliability of tractography schemes and make it possible to correctly identify the neural connections between functionally connected regions of the nervous system.

Evren Özarslan, Timothy M. Shepherd, Baba C. Vemuri, Stephen J. Blackband, Thomas H. Mareci
An Automated Approach to Connectivity-Based Partitioning of Brain Structures

We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres

in vivo

, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.

P. A. Cook, H. Zhang, B. B. Avants, P. Yushkevich, D. C. Alexander, J. C. Gee, O. Ciccarelli, A. J. Thompson
Deformable Registration of Diffusion Tensor MR Images with Explicit Orientation Optimization

In this paper we present a novel deformable registration algorithm for diffusion tensor (DT) MR images that enables explicit analytic optimization of tensor reorientation. The optimization seeks a piecewise affine transformation that divides the image domain into uniform regions and transforms each of them affinely. The objective function captures both the image similarity and the smoothness of the transformation across region boundaries. The image similarity enables explicit orientation optimization by incorporating tensor reorientation, which is necessary for warping DT images. The objective function is formulated in a way that allows explicit implementation of analytic derivatives to drive fast and accurate optimization using the conjugate gradient method. The optimal transformation is hierarchically refined in a subdivision framework. A comparison with affine registration for inter-subject normalization of 8 subjects shows that our algorithm improves the alignment of manually segmented white matter structures (corpus callosum and cortio-spinal tracts).

Hui Zhang, Paul A. Yushkevich, James C. Gee
A Hamilton-Jacobi-Bellman Approach to High Angular Resolution Diffusion Tractography

This paper describes a new framework for white matter tractography in high angular resolution diffusion data. A direction-dependent local cost is defined based on the diffusion data for every direction on the unit sphere. Minimum cost curves are determined by solving the Hamilton-Jacobi-Bellman using an efficient algorithm. Classical costs based on the diffusion tensor field can be seen as a special case. While the minimum cost (or equivalently the travel time of a particle moving along the curve) and the anisotropic front propagation frameworks are related, front speed is related to particle speed through a Legendre transformation which can severely impact anisotropy information for front propagation techniques. Implementation details and results on high angular diffusion data show that this method can successfully take advantage of the increased angular resolution in high b-value diffusion weighted data despite lower signal to noise ratio.

Eric Pichon, Carl-Fredrik Westin, Allen R. Tannenbaum
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their

B

-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.

Mahnaz Maddah, Andrea U. J. Mewes, Steven Haker, W. Eric L. Grimson, Simon K. Warfield
MR Diffusion-Based Inference of a Fiber Bundle Model from a Population of Subjects

This paper proposes a method to infer a high level model of the white matter organization from a population of subjects using MR diffusion imaging. This method takes as input for each subject a set of trajectories stemming from any tracking algorithm. Then the inference results from two nested clustering stages. The first clustering converts each individual set of trajectories into a set of bundles supposed to represent large white matter pathways. The second clustering matches these bundles across subjects in order to provide a list of candidates for the bundle model. The method is applied on a population of eleven subjects and leads to the inference of 17 such candidates.

V. El Kouby, Y. Cointepas, C. Poupon, D. Rivière, N. Golestani, J. -B. Poline, D. Le Bihan, J. -F. Mangin
Knowledge-Based Classification of Neuronal Fibers in Entire Brain

This work presents a framework driven by parcellation of brain gray matter in standard normalized space to classify the neuronal fibers obtained from diffusion tensor imaging (DTI) in entire human brain. Classification of fiber bundles into groups is an important step for the interpretation of DTI data in terms of functional correlates of white matter structures. Connections between anatomically delineated brain regions that are considered to form functional units, such as a short-term memory network, are identified by first clustering fibers based on their terminations in anatomically defined zones of gray matter according to Talairach Atlas, and then refining these groups based on geometric similarity criteria. Fiber groups identified this way can then be interpreted in terms of their functional properties using knowledge of functional neuroanatomy of individual brain regions specified in standard anatomical space, as provided by functional neuroimaging and brain lesion studies.

Yan Xia, And U. Turken, Susan L. Whitfield-Gabrieli, John D. Gabrieli
A Physical Model for DT-MRI Based Connectivity Map Computation

In this study we address the problem of extracting a robust connectivity metric for brain white matter. We defined the connectivity problem as an energy minimization task, by associating the DT-field to a physical system composed of nodes and springs, with their constants defined as a function of local structure. Using a variational approach we formulated a fast and stable map evolution, which utilizes an anisotropic kernel smoothing scheme equivalent to a diffusion PDE. The proposed method provides connectivity maps that correlate with normal anatomy on real patient data.

Erdem Yörük, Burak Acar, Roland Bammer

Image Segmentation and Analysis I

A Novel 3D Partitioned Active Shape Model for Segmentation of Brain MR Images

A 3D Partitioned Active Shape Model (PASM) is proposed in this paper to address the problems of the 3D Active Shape Models (ASM). When training sets are small. It is usually the case in 3D segmentation, 3D ASMs tend to be restrictive. This is because the allowable region spanned by relatively few eigenvectors cannot capture the full range of shape variability. The 3D PASM overcomes this limitation by using a partitioned representation of the ASM. Given a Point Distribution Model (PDM), the mean mesh is partitioned into a group of small tiles. In order to constrain deformation of tiles, the statistical priors of tiles are estimated by applying Principal Component Analysis to each tile. To avoid the inconsistency of shapes between tiles, training samples are projected as curves in one hyperspace instead of point clouds in several hyperspaces. The deformed points are then fitted into the allowable region of the model by using a curve alignment scheme. The experiments on 3D human brain MRIs show that when the numbers of the training samples are limited, the 3D PASMs significantly improve the segmentation results as compared to 3D ASMs and 3D Hierarchical ASMs.

Zheen Zhao, Stephen R. Aylward, Eam Khwang Teoh
Cross Entropy: A New Solver for Markov Random Field Modeling and Applications to Medical Image Segmentation

This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulation, is general and stochastic. Unlike some popular optimization methods such as belief propagation and graph cuts, CE makes no assumption on the form of objective functions and thus can be applied to any type of MRF models. Furthermore, it achieves higher performance of finding more global optima because of its stochastic property. In addition, it is more efficient than other stochastic methods like simulated annealing. We tested the new solver in 4 series of segmentation experiments on synthetic and clinical, vascular and cerebral images. The experiments show that CE can give more accurate segmentation results.

Jue Wu, Albert C. S. Chung
Semi-automated Basal Ganglia Segmentation Using Large Deformation Diffeomorphic Metric Mapping

This paper investigates the techniques required to produce accurate and reliable segmentations via grayscale image matching. Finding a large deformation, dense, non-rigid transformation from a template image to a target image allows us to map a template segmentation to the target image space, and therefore compute the target image segmentation and labeling. We outline a semi-automated procedure involving landmark and image intensity-based matching via the large deformation diffeomorphic mapping metric (LDDMM) algorithm. Our method is applied specifically to the segmentation of the caudate nucleus in pre- and post-symptomatic Huntington’s Disease (HD) patients. Our accuracy is compared against gold-standard manual segmentations and various automated segmentation tools through the use of several error metrics.

Ali Khan, Elizabeth Aylward, Patrick Barta, Michael Miller, M. Faisal Beg
Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.

Charles Florin, Nikos Paragios, Jim Williams
Hybrid Segmentation Framework for Tissue Images Containing Gene Expression Data

Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the over 20,000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this work, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our method utilizes shape models from training images, texture differentiation at region boundaries, and features of anatomical landmarks, to deform a subdivision mesh-based atlas to fit gene expression images. The subdivision mesh provides a common coordinate system for internal brain data through which gene expression patterns can be compared across images. The automated large-scale annotation will help scientists interpret gene expression patterns at cellular resolution more efficiently.

Musodiq Bello, Tao Ju, Joe Warren, James Carson, Wah Chiu, Christina Thaller, Gregor Eichele, Ioannis A. Kakadiaris
Fully Automatic Kidneys Detection in 2D CT Images: A Statistical Approach

In this paper, we focus on automatic kidneys detection in 2D abdominal computed tomography (CT) images. Identifying abdominal organs is one of the essential steps for visualization and for providing assistance in teaching, clinical training and diagnosis. It is also a key step in medical image retrieval application. However, due to gray levels similarities of adjacent organs, contrast media effect and relatively high variation of organ’s positions and shapes, automatically identifying abdominal organs has always been a challenging task. In this paper, we present an original method, in a statistical framework, for fully automatic kidneys detection. It makes use of spatial and gray-levels prior models built using a set of training images. The method is tested on over 400 clinically acquired images and very promising results are obtained.

Wala Touhami, Djamal Boukerroui, Jean-Pierre Cocquerez
Segmentation of Neighboring Organs in Medical Image with Model Competition

This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum

a posteriori

(MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.

Pingkun Yan, Weijia Shen, Ashraf A. Kassim, Mubarak Shah
Point-Based Geometric Deformable Models for Medical Image Segmentation

Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed necessary. In this paper, we present the idea of performing level set evolution in a point-based environment where the sampling location and density of the domains are adaptively determined by level set geometry and image information, thus rid of the needs for computational grids and the associated refinements. We have implemented the general geometric deformable models using this representation and computational strategy, including the incorporation of region-based prior information in both domain sampling and curve evolution processes, and have evaluated the performance of the method on synthetic data with ground truth and performed surface segmentation of brain structures from three-dimensional magnetic resonance images.

Hon Pong Ho, Yunmei Chen, Huafeng Liu, Pengcheng Shi
A Variational PDE Based Level Set Method for a Simultaneous Segmentation and Non-rigid Registration

A new variational PDE based level set method for a simultaneous image segmentation and non-rigid registration using prior shape and intensity information is presented. The segmentation is obtained by finding a non-rigid registration to the prior shape. The non-rigid registration consists of both a global rigid transformation and a local non-rigid deformation. In this model, a prior shape is used as an initial contour which leads to decrease the numerical calculation time. The model is tested against two chamber end systolic ultrasound images from thirteen human patients. The experimental results provide preliminary evidence of the effectiveness of the model in detecting the boundaries of the incompletely resolved objects which were plagued by noise, dropout, and artifact.

Jung-ha An, Yunmei Chen, Feng Huang, David Wilson, Edward Geiser
A Tracking Approach to Parcellation of the Cerebral Cortex

The cerebral cortex is composed of regions with distinct laminar structure. Functional neuroimaging results are often reported with respect to these regions, usually by means of a brain “atlas”. Motivated by the need for more precise atlases, and the lack of model-based approaches in prior work in the field, this paper introduces a novel approach to parcellating the cortex into regions of distinct laminar structure, based on the theory of target tracking. The cortical layers are modelled by hidden Markov models and are tracked to determine the Bayesian evidence of layer hypotheses. This model-based parcellation method, evaluated here on a set of histological images of the cortex, is extensible to 3-D images.

Chris Adamson, Leigh Johnston, Terrie Inder, Sandra Rees, Iven Mareels, Gary Egan
Cell Segmentation, Tracking, and Mitosis Detection Using Temporal Context

The Large Scale Digital Cell Analysis System (LSDCAS) developed at the University of Iowa provides capabilities for extended-time live cell image acquisition. This paper presents a new approach to quantitative analysis of live cell image data. By using time as an extra dimension, level set methods are employed to determine cell trajectories from 2D + time data sets. When identifying the cell trajectories, cell cluster separation and mitotic cell detection steps are performed. Each of the trajectories corresponds to the motion pattern of an individual cell in the data set. At each time frame, number of cells, cell locations, cell borders, cell areas, and cell states are determined and recorded. The proposed method can help solving cell analysis problems of general importance including cell pedigree analysis and cell tracking. The developed method was tested on cancer cell image sequences and its performance compared with manually-defined ground truth. The similarity Kappa Index is 0.84 for segmentation area and the signed border positioning segmentation error is 1.6 ± 2.1

μ

m.

Fuxing Yang, Michael A. Mackey, Fiorenza Ianzini, Greg Gallardo, Milan Sonka
A Unifying Approach to Registration, Segmentation, and Intensity Correction

We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems.

Kilian M. Pohl, John Fisher, James J. Levitt, Martha E. Shenton, Ron Kikinis, W. Eric L. Grimson, William M. Wells
Automatic 3D Segmentation of Intravascular Ultrasound Images Using Region and Contour Information

Intravascular ultrasound (IVUS) produces images of arteries that show the lumen in addition to the layered structure of the vessel wall. A new automatic 3D IVUS fast-marching segmentation model is presented. The method is based on a combination of region and contour information, namely the gray level probability density functions (PDFs) of the vessel structures and the image gradient. Accurate results were obtained on in-vivo and simulated data with average point to point distances between detected vessel wall boundaries and validation contours below 0.105 mm. Moreover, Hausdorff distances (that represent the worst point to point variations) resulted in values below 0.344 mm, which demonstrate the potential of combining region and contour information in a fast-marching scheme for 3D automatic IVUS image processing.

Marie-Hélène Roy Cardinal, Jean Meunier, Gilles Soulez, Roch L. Maurice, Éric Thérasse, Guy Cloutier
Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme

Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.

Jenny Folkesson, Erik Dam, Ole Fogh Olsen, Paola Pettersen, Claus Christiansen
Automatic Segmentation of the Left Ventricle in 3D SPECT Data by Registration with a Dynamic Anatomic Model

We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. finding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour.

Finding of the LV is done by flexible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors.

Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.

Lars Dornheim, Klaus D. Tönnies, Kat Dixon
Intravascular Ultrasound-Based Imaging of Vasa Vasorum for the Detection of Vulnerable Atherosclerotic Plaque

Vulnerable plaques are dangerous atherosclerotic lesions that bear a high risk of complications that can lead to heart attacks and strokes. These plaques are known to be chronically inflamed. The vasa vasorum (VV) are microvessels that nourish vessel walls. Proliferation of VV is part of the “response to injury” phenomenon in the process of plaque formation. Recent evidence has shown strong correlations between neovessel formation and macrophage infiltration in atherosclerotic plaque, suggesting VV density as a surrogate marker of plaque inflammation and vulnerability. We have developed a novel method for imaging and analyzing the density and perfusion of VV in human coronary atherosclerotic plaques using intravascular ultrasound (IVUS). Images are taken during the injection of a microbubble contrast agent and the spatiotemporal changes of the IVUS signal are monitored using enhancement-detection techniques. We present analyses of

in vivo

human coronary cases that, for the first time, demonstrate the feasibility of IVUS imaging of VV.

Sean M. O’Malley, Manolis Vavuranakis, Morteza Naghavi, Ioannis A. Kakadiaris
Parametric Response Surface Models for Analysis of Multi-site fMRI Data

Analyses of fMRI brain data are often based on statistical tests applied to each voxel or use summary statistics within a region of interest (such as mean or peak activation). These approaches do not explicitly take into account spatial patterns in the activation signal. In this paper, we develop a response surface model with parameters that directly describe the spatial shapes of activation patterns. We present a stochastic search algorithm for parameter estimation. We apply our method to data from a multi-site fMRI study, and show how the estimated parameters can be used to analyze different sources of variability in image generation, both qualitatively and quantitatively, based on spatial activation patterns.

Seyoung Kim, Padhraic Smyth, Hal Stern, Jessica Turner

Clinical Applications – Validation

Subject Specific Finite Element Modelling of the Levator Ani

Understanding of the dynamic behaviour of the levator ani is important to the assessment of pelvic floor dysfunction. Whilst shape modelling allows the depiction of 3D morphological variation of the levator ani between different patient groups, it is insufficient to determine the underlying behaviour of how the muscle deforms during contraction and strain. The purpose of this study is to perform a subject specific finite element analysis of the levator ani with open access magnetic resonance imaging. The method is based on a Mooney-Rivlin hyperelastic model and permits dynamic study of subjects under natural physiological loadings. The value of the proposed modelling framework is demonstrated with dynamic 3D data from nulliparous, female subjects.

Su-Lin Lee, Ara Darzi, Guang-Zhong Yang
Robust Visualization of the Dental Occlusion by a Double Scan Procedure

A detailed visualization of the dental occlusion in 3D image-based planning environments for oral and maxillofacial planning is important. With CT imaging however, this occlusion is often deteriorated by streak artifacts caused by amalgam fillings. Moreover, more detailed surface information at the level of the dental cuspids is often desired.

In this paper, a double scan technique is introduced to image the dental occlusion by means of a newly designed 3D splint. The patient wears this splint between the upper and lower teeth during CT-scan. In a second step, the splint is positioned between the plaster casts of the upper and lower jaw, and this setup is scanned. Based on markers in the 3D splint, both data sets are fused and a combined visualization is possible. The accuracy, robustness and applicability in clinical routine is shown.

This technology enables meticulous 3D cephalometric analysis, detailed maxillofacial planning and opens possibilities towards intraoperative support.

Filip Schutyser, Gwen Swennen, Paul Suetens
Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set

Focal cortical dysplasia (FCD), a malformation of cortical development, is an important cause of medically intractable epilepsy. FCD lesions are difficult to distinguish from non-lesional cortex and their delineation on MRI is a challenging task. This paper presents a method to segment FCD lesions on T1-weighted MRI, based on a 3D deformable model, implemented using the level set framework. The deformable model is driven by three MRI features: cortical thickness, relative intensity and gradient. These features correspond to the visual characteristics of FCD and allow to differentiate lesions from normal tissues. The proposed method was tested on 18 patients with FCD and its performance was quantitatively evaluated by comparison with the manual tracings of two trained raters. The validation showed that the similarity between the level set segmentation and the manual labels is similar to the agreement between the two human raters. This new approach may become a useful tool for the presurgical evaluation of patients with intractable epilepsy.

O. Colliot, T. Mansi, N. Bernasconi, V. Naessens, D. Klironomos, A. Bernasconi
Effects of Healthy Aging Measured By Intracranial Compartment Volumes Using a Designed MR Brain Database

A publicly available database of high-quality, multi-modal MR brain images of carefully screened healthy subjects, equally divided by sex, and with an equal number of subjects per age decade, would be of high value to investigators interested in the statistical study of disease. This report describes initial use of an accumulating healthy database currently comprising 50 subjects aged 20-72. We examine changes by age and sex to the volumes of gray matter, white matter and cerebrospinal fluid for subjects within the database. We conclude that traditional views of healthy aging should be revised. Significant atrophy does not appear in healthy subjects 60 or 70 years old. Gray matter loss is not restricted to senility, but begins in early adulthood and is progressive. The percentage of white matter increases with age. A carefully-designed healthy database should be useful in the statistical analysis of many age- and non-age- related diseases.

Bénédicte Mortamet, Donglin Zeng, Guido Gerig, Marcel Prastawa, Elizabeth Bullitt
Predicting Clinical Variable from MRI Features: Application to MMSE in MCI

The ability to predict a clinical variable from automated analysis of single, cross-sectional T1-weighted (T1w) MR scans stands to improve the management of patients with neurological diseases. We present a methodology for predicting yearly Mini-Mental Score Examination (MMSE) changes in Mild Cognitive Impairment (MCI) patients. We begin by generating a non-pathological, multidimensional reference space from a group of 152 healthy volunteers by Principal Component Analyses of (i) T1w MR intensity of linearly registered Volumes of Interest (VOI); and (ii) trace of the deformation fields of nonlinearly registered VOIs. We use multiple regression to build linear models from eigenvectors where the projection eigencoordinates of patient data in the reference space are highly correlated with the clinical variable of interest. In our cohort of 47 MCI patients, composed of 16 decliners, 26 stable and 5 improvers (based on MMSE at 1 yr follow-up), there was a significant difference (

P

= 0.0003) for baseline MMSE scores between decliners and improvers, but no other differences based on age or sex. First, we classified our three groups using leave-one-out, forward stepwise linear discriminant analyses of the projection eigencoordinates with 100% accuracy. Next, we compared various linear models by computing F-statistics on the residuals of predicted

vs

actual values. The best model was based on 10 eigenvectors + baseline MMSE, with predicted yearly changes highly correlated (

r

= 0.6955) with actual data. Prospective study of an independent cohort of patients is the next logical step towards establishing this promising technique for clinical use.

S. Duchesne, A. Caroli, C. Geroldi, G. B. Frisoni, D. Louis Collins
Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images

Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.

Ashraf Mohamed, Christos Davatzikos
STREM: A Robust Multidimensional Parametric Method to Segment MS Lesions in MRI

We propose to segment Multiple Sclerosis (MS) lesions overtime in multidimensional Magnetic Resonance (MR) sequences. We use a robust algorithm that allows the segmentation of the abnormalities using the whole time series simultaneously and we propose an original rejection scheme for outliers. We validate our method using the BrainWeb simulator. To conclude, promising preliminary results on longitudinal multi-sequences of clinical data are shown.

L. S. Aït-Ali, S. Prima, P. Hellier, B. Carsin, G. Edan, C. Barillot
Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatment of Parkinson’s disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

F. Javier Sánchez Castro, Claudio Pollo, Reto Meuli, Philippe Maeder, Meritxell Bach Cuadra, Olivier Cuisenaire, Jean-Guy Villemure, Jean-Philippe Thiran
Localization of Abnormal Conduction Pathways for Tachyarrhythmia Treatment Using Tagged MRI

Tachyarrhythmias are pathological fast heart rhythms often caused by abnormally conducting myocardial areas (foci). Treatment by radio-frequency (RF) ablation uses electrode-catheters to monitor and destroy foci.The procedure is normally guided with x-rays (2D), and thus prone to errors in location and excessive radiation exposure. Our main goal is to provide pre- and intra-operative 3D MR guidance in XMR systems by locating the abnormal conduction pathways. We address the inverse electro-mechanical relation by using motion in order to infer electrical propagation. For this purpose we define a probabilistic measure of the onset of regional myocardial activation, derived from 3D motion fields obtained by tracking tagged MR sequences with non-rigid registration. Activation isochrones are then derived to determine activation onset.

We also compare regional motion between two different image acquisitions, thus assisting in diagnosing arrhythmia, in follow up of treatment, and in determining whether the ablation was successful.Difference maps of isochrones and other motion descriptors are computed to determine abnormal patterns. Validation was carried out using an electro-mechanical model of the heart, synthetic data, a cardiac MRI atlas of motion and geometry, MRI data from 6 healthy volunteers (one of them subjected to stress), and an MRI study on a patient with tachyarrhythmia, before and after RF ablation. A pre-operative MRI study on a second patient with tachyarrhythmia was used to test the methodology in a clinical scenario, predicting the abnormally conducting region.

G. I. Sanchez-Ortiz, M. Sermesant, K. S. Rhode, R. Chandrashekara, R. Razavi, D. L. G. Hill, D. Rueckert
Automatic Mammary Duct Detection in 3D Ultrasound

This paper presents a method for the initial detection of ductal structures within 3D ultrasound images using second-order shape measurements. The desire to detect ducts is motivated in a number of way, principally as step in the detection and assessment of ductal carcinoma in-situ. Detection is performed by measuring the variation of the local second-order shape from a prototype shape corresponding to a perfect tube. We believe this work is the first demonstration of the ability to detect sections of duct automatically in ultrasound images. The detection is performed with a view to employing vessel tracking method to delineate the full ductal structure.

Mark J. Gooding, Matthew Mellor, Jacqueline A Shipley, Kathy A Broadbent, Dorothy A Goddard
Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

We have been developing an approach for automatically quantifying organ motion for adaptive radiation therapy of the prostate. Our approach is based on deformable image registration, which makes it possible to establish a correspondence between points in images taken on different days. This correspondence can be used to study organ motion and to accumulate inter-fraction dose. In prostate images, however, the presence of bowel gas can cause significant correspondence errors. To account for this problem, we have developed a novel method that combines large deformation image registration with a bowel gas segmentation and deflation algorithm. In this paper, we describe our approach and present a study of its accuracy for adaptive radiation therapy of the prostate. All experiments are carried out on 3-dimensional CT images.

B. C. Davis, M. Foskey, J. Rosenman, L. Goyal, S. Chang, S. Joshi
Inter-Operator Variability in Perfusion Assessment of Tumors in MRI Using Automated AIF Detection

A method is presented for the calculation of perfusion parameters in dynamic contrast enhanced MRI. This method requires identification of enhancement curves for both tumor tissue and plasma. Inter-operator variability in the derived rate constant between plasma and extra-cellular extra-vascular space is assessed in both canine and human subjects using semi-automated tumor margin identification with both manual and automated arterial input function (AIF) identification. Experimental results show a median coefficient of variability (CV) for parameter measurement with manual AIF identification of 21.5% in canines and 11% in humans, with a median CV for parameter measurement with automated AIF identification of 6.7% in canines and 6% in humans.

Edward Ashton, Teresa McShane, Jeffrey Evelhoch
Computer–Assisted Deformity Correction Using the Ilizarov Method

The Taylor spatial frame is a fixation device used to implement the Ilizarov method of bone deformity correction to gradually distract an osteotomized bone at regular intervals, according to a prescribed schedule. We improve the accuracy of Ilizarov’s method of osteogenesis by preoperatively planning the correction, intraoperatively measuring the location of the frame relative to the patient, and computing the final shape of the frame. In four of five tibial phantom experiments, we were able to achieve correction errors of less than 2 degrees of total rotation. We also demonstrate how registration uncertainty can be propagated through the planned transformation to visualize the range of possible correction outcomes. Our method is an improvement over an existing computer–assisted technique (Iyun

et al.

[3]) in that the surgeon has the same flexibility as in the conventional technique when fixating the frame to the patient.

A. L. Simpson, B. Ma, D. P. Borschneck, R. E. Ellis
Real-Time Interactive Viewing of 4D Kinematic MR Joint Studies

Assessment of soft tissue in normal and abnormal joint motion today gets feasible by acquiring time series of 3D MRI images. However, slice-by-slice viewing of such 4D kinematic images is cumbersome, and does not allow appreciating the movement in a convenient way. Simply presenting slice data in a cine-loop will be compromised by through-plane displacements of anatomy and “jerks” between frames, both of which hamper visual analysis of the movement. To overcome these limitations, we have implemented a demonstrator for viewing 4D kinematic MRI datasets. It allows to view any user defined anatomical structure from any viewing perspective in real-time. Smoothly displaying the movement in a cine-loop is realized by image post processing, fixing any user defined anatomical structure after image acquisition.

Heinrich Schulz, Kirsten Meetz, Clemens Bos, Daniel Bystrov, Thomas Netsch
Computer-Assisted Ankle Joint Arthroplasty Using Bio-engineered Autografts

Bio-engineered cartilage has made substantial progress over the last years. Preciously few cases, however, are known where patients were actually able to benefit from these developments. In orthopaedic surgery, there are two major obstacles between in-vitro cartilage engineering and its clinical application: successful integration of an autologuous graft into a joint and the high cost of individually manufactured implants. Computer Assisted Surgery techniques can potentially address both issues at once by simplifying the therapy, allowing pre-fabrication of bone grafts according to a shape model, individual operation planning based on CT images and providing optimal accuracy during the intervention. A pilot study was conducted for the ankle joint, comprising a simplified rotational symmetric bone surface model, a dedicated planning software and a complete cycle of treatment on one cadaveric human foot. The outcome was analysed using CT and MRI images; the post-operative CT was further segmented and registered with the implant shape to prove the feasibility of computer assisted arthroplasty using bio-engineered autografts.

R. Sidler, W. Köstler, T. Bardyn, M. A. Styner, N. Südkamp, L. Nolte, M. Á. González Ballester
Prospective Head Motion Compensation for MRI by Updating the Gradients and Radio Frequency During Data Acquisition

Subject motion appears to be a limiting factor in numerous magnetic resonance imaging (MRI) applications. For head imaging the subject’s ability to maintain the same head position for a considerable period of time places restrictions on the total acquisition time. For healthy individuals this time typically does not exceed 10 minutes and may be considerably reduced in case of pathology. In particular, head tremor, which often accompanies stroke, may render certain high-resolution 2D and 3D techniques inapplicable. Several navigator techniques have been proposed to circumvent the subject motion problem. The most suitable for head imaging appears to be the orbital or spherical navigator methods. Navigators, however, not only lengthen the measurement because of the time required for acquisition of the position information, but also require additional excitation radio frequency (RF) pulses to be incorporated into the sequence timing, which disturbs the steady state. Here we demonstrate the possibility of interfacing the MR scanner with an external optical motion tracking system, capable of determining the object’s position with sub-millimeter accuracy and an update rate of 60Hz. The movement information on the object position (head) is used to compensate the motion in real time. This is done by updating the field of view (FOV) by recalculating the gradients and the RF-parameter of the MRI tomograph during the acquisition of k-space data based on the tracking data. Results of rotation phantom, in vivo experiments and the implementation in two different MRI sequences are presented.

Christian Dold, Maxim Zaitsev, Oliver Speck, Evelyn A. Firle, Jürgen Hennig, Georgios Sakas
Harmonic Skeleton Guided Evaluation of Stenoses in Human Coronary Arteries

This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.

Yan Yang, Lei Zhu, Steven Haker, Allen R. Tannenbaum, Don P. Giddens
Acquisition-Related Limitations in MRI Based Morphometry

Although significant effort has been spent over the past decades to develop innovative image processing algorithms and to improve existing methods in terms of precision, reproducibility and computational efficiency, relatively few research was undertaken to find out to what extent the validity of results obtained with these methods is limited by inherent imperfections of the input images. This observation is especially true for MRI based morphometry, which aims at precise and highly reproducible determination of geometrical properties of anatomical structures despite the fact that MR images are geometrically distorted. We here present (a) a method for characterization of site-specific geometrical distortions and (b) the results of a long term study designed to find out how precisely geometrical properties and morphological changes of brain structures can, in principle, be detected in images acquired with MRI scanners. Due to the long-term character of our study, our findings include effects resulting from limited hardware stability as well as from variations in patient positioning. Our results show that these effects can be strong enough to substantially confound MRI studies of small morphological changes.

Arne Littmann, Jens Guehring, Christian Buechel, Hans-Siegfried Stiehl
Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Steven Haker, William M. Wells III, Simon K. Warfield, Ion-Florin Talos, Jui G. Bhagwat, Daniel Goldberg-Zimring, Asim Mian, Lucila Ohno-Machado, Kelly H. Zou
Two Methods for Validating Brain Tissue Classifiers

In this paper, we present an evaluation of seven automatic brain tissue classifiers based on level of agreements. A number of agreement measures are explained, and we show how they can be used to compare different segmentation techniques. We use the Simultaneous Truth and Performance Level Estimation (STAPLE) of Warfield et al. but also introduce a novel evaluation technique based on the Williams’ index. The methods are evaluated using these two techniques on a population of forty subjects, each having an SPGR scan and a co-registered T2 weighted scan. We provide an interpretation of the results and show how similar the output of the STAPLE analysis and Williams’ index are. When no ground truth is required, we recommend the use of Williams’ index as it is easy and fast to compute.

Marcos Martin-Fernandez, Sylvain Bouix, Lida Ungar, Robert W. McCarley, Martha E. Shenton
Comparison of Vessel Segmentations Using STAPLE

We propose a novel method for the validation of vascular segmentations. Our technique combines morphological operators and the STAPLE algorithm to obtain ground truth of centerline extractions as well as a measure of accuracy of the methods to be compared. Moreover, our method can be extended to the validation of any open-curves. We also present a comparison study of three vascular segmentation methods: ridge traversal, statistical and curves level set. They are compared with manual segmentations from five experts.

Julien Jomier, Vincent LeDigarcher, Stephen R. Aylward
Validation Framework of the Finite Element Modeling of Liver Tissue

In this work, we aim at validating some soft tissue deformation models using high resolution Micro Computed Tomography (Micro-CT) and medium resolution Cone-Beam CT (CBCT) images. These imaging techniques play a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) even for small force loads, and provide good capabilities for creating accurate 3D models of tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite element (F.E.) models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remains a challenge. For our validation, ex vivo lamb liver is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We found that the LEM is more applicable to lamb liver than the LVEM for small force loads (<40

g

) and that the NHM is closer to reality than the LVEM for this same range of force loads.

Hongjian Shi, Fahmi Rachid, Aly A. Farag
A Complete Augmented Reality Guidance System for Liver Punctures: First Clinical Evaluation

We provided in [14] an augmented reality guidance system for liver punctures, which has been validated on a static abdominal phantom [16]. In this paper, we report the first in vivo experiments.

We developed a strictly passive protocol to directly evaluate our system on patients. We show that the system algorithms work efficiently and we highlight the clinical constraints that we had to overcome (small operative field, weight and sterility of the tracked marker attached to the needle...). Finally, we investigate to what extent breathing motion can be neglected for free breathing patient. Results show that the guiding accuracy, close to 1 cm, is sufficient for large targets only (above 3 cm of diameter) when the breathing motion is neglected. In the near future, we aim at validating our system on smaller targets using a respiratory gating technique.

S. A. Nicolau, X. Pennec, L. Soler, N. Ayache

Imaging Systems – Visualization

A Novel Approach to High Resolution Fetal Brain MR Imaging

This paper describes a novel approach to forming high resolution MR images of the human fetal brain. It addresses the key problem of motion of the fetus by proposing a registration refined compounding of multiple sets of orthogonal fast 2D MRI slices, that are currently acquired for clinical studies, into a single high resolution MRI volume. A robust multi-resolution slice alignment is applied iteratively to the data to correct motion of the fetus that occurs between 2D acquisitions. This is combined with an intensity correction step and a super resolution reconstruction step, to form a single high isotropic resolution volume of the fetal brain. Experimental validation on synthetic image data with known motion types and underlying anatomy, together with retrospective application to sets of clinical acquisitions are included. Results indicate the method promises a unique route to acquiring high resolution MRI of the fetal brain in vivo allowing comparable quality to that of neonatal MRI. Such data is highly valuable in allowing a clinically applicable window into the process of normal and abnormal brain development.

F. Rousseau, O. Glenn, B. Iordanova, C. Rodriguez-Carranza, D. Vigneron, J. Barkovich, C. Studholme
Respiratory Signal Extraction for 4D CT Imaging of the Thorax from Cone-Beam CT Projections

Current methods of four-dimensional (4D) CT imaging of the thorax synchronise the acquisition with a respiratory signal to restrospectively sort acquired data. The quality of the 4D images relies on an accurate description of the position of the thorax in the respiratory cycle by the respiratory signal. Most of the methods used an external device for acquiring the respiratory signal. We propose to extract it directly from thorax cone-beam (CB) CT projections. This study implied two main steps: the simulation of a set of CBCT projections, and the extraction, selection and integration of motion information from the simulation output to obtain the respiratory signal. A real respiratory signal was used for simulating the CB acquisition of a breathing patient. We extracted from CB images a respiratory signal with 96.4% linear correlation with the reference signal, but we showed that other measures of the quality of the extracted respiratory signal were required.

Simon Rit, David Sarrut, Chantal Ginestet
Registering Liver Pathological Images with Prior In Vivo CT/MRI Data

Liver transplantation affords a unique opportunity to assess and improve radiological imaging of the liver, as the full explanted liver is available for review and comparison. Quantitative comparison between the explanted liver and in vivo images acquired prior to transplantation requires accurate registration of images of the explanted liver to the radiological images. However, this registration problem is challenging because the orientation change and the deformation magnitude between the two image sets exceed the level assumed for most registration algorithms. This paper suggests a two-step registration process to overcome the difficulty: to first align the orientation of 3D liver models built from two sets of image data using maximum volume overlap as their similarity measurement, and second to deform one model to match the other. The key contribution of this paper is that it utilizes the global volumetric information and the asymmetry property of the liver model to determinately provide a simple and reliable initial point for further deformable model based registration. Our experimental data demonstrate the effectiveness of this approach.

Huadong Wu, Alyssa M. Krasinskas, Mitchell E. Tublin, Brian E. Chapman
Support Vector Clustering for Brain Activation Detection

In this paper, we propose a new approach to detect activated time series in functional MRI using

support vector clustering (SVC)

. We extract Fourier coefficients as the features of fMRI time series and cluster these features by SVC. In SVC, these features are mapped from their original

feature space

to a very high dimensional

kernel space

. By finding a compact sphere that encloses the mapped features in the kernel space, one achieves a set of cluster boundaries in the feature space. The SVC is an effective and robust fMRI activation detection method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality detection results without explicitly specifying the number of clusters, (3) the stronger robustness due to the mechanism in outlier elimination. Experimental results on simulated and real fMRI data demonstrate the effectiveness of SVC.

Defeng Wang, Lin Shi, Daniel S. Yeung, Pheng-Ann Heng, Tien-Tsin Wong, Eric C. C. Tsang
Inter-frame Motion Correction for MR Thermometry

Noninvasive temperature measurement is feasible with MRI to monitor changes in thermal therapy. Phase shift based MR thermometry gives an estimate of the relative temperature variation between thermal and baseline images. This technique is limited, however, when applied on targets under inter-frame motion. Simple image registration and subtraction are not adequate to recover the temperature properly since the phase shift due to temperature changes is corrupted by an unwanted phase shift. In this work, the unwanted phase shift is predicted from the raw registered phase shift map itself. To estimate the unwanted phase shift, a thin plate smoothing spline is fitted to the values

outside

the heated region. The spline value

in

the heated area serves as an estimate for the offset. The estimation result is applied to correct errors in the temperature maps of an ex-vivo experiment.

S. Suprijanto, M. W. Vogel, F. M. Vos, H. A. Vrooman, A. M. Vossepoel
Adaptive Multiscale Ultrasound Compounding Using Phase Information

The recent availability of real-time three-dimensional echocardiography offers a convenient, low-cost alternative for detection and diagnosis of heart pathologies. However, a complete description of the heart can be obtained only by combining the information provided by different acoustic windows. We present a new method for compounding 3D ultrasound scans acquired from different views. The method uses multiscale information about local structure definition and orientation to weight the contributions of the images. We propose to use image phase to obtain these image characteristics while keeping invariance to image contrast. The monogenic signal provides a convenient, integrated approach for this purpose. We have evaluated our algorithm on synthetic images and heart scans from volunteers, showing it provides a significant improvement in image quality when compared to traditional compounding methods.

Vicente Grau, J. Alison Noble
3D Freehand Ultrasound Reconstruction Based on Probe Trajectory

3D freehand ultrasound imaging is a very attractive technique in medical examinations and intra-operative stage for its cost and field of view capacities. This technique produces a set of non parallel B-scans which are irregularly distributed in the space. Reconstruction amounts to computing a regular lattice volume and is needed to apply conventional computer vision algorithms like registration. In this paper, a new 3D reconstruction method is presented, taking explicitly into account the probe trajectory. Experiments were conducted on different data sets with various probe motion types and indicate that this technique outperforms classical methods, especially on low acquisition frame rate.

Pierrick Coupé, Pierre Hellier, Noura Azzabou, Christian Barillot
Self-Calibrating Ultrasound-to-CT Bone Registration

We describe a new self-calibrating approach to rigid registration of 3D ultrasound images in which

in vivo

data acquired for registration are used to simultaneously perform a patient-specific update of the calibration parameters of the 3D ultrasound system. Using a self-calibrating implementation of a point-based registration algorithm, and points obtained from ultrasound images of the femurs and pelves of human cadavers, we show that the accuracy of registration to a CT scan is significantly improved compared with a standard algorithm. This new approach provides an effective means of compensating for errors introduced by the propagation of ultrasound through soft tissue, which currently limit the accuracy of conventional methods where the calibration parameters are fixed to values determined preoperatively using a phantom.

Dean C. Barratt, Graeme Penney, Carolyn S. K. Chan, Mike Slomczykowski, Timothy J. Carter, Philip J. Edwards, David J. Hawkes
A Hand-Held Probe for Vibro-Elastography

Vibro-elastography is a new medical imaging method that identifies the mechanical properties of tissue by measuring tissue motion in response to a multi-frequency external vibration source. Previous research on vibro-elastography used ultrasound to measure the tissue motion and system identification techniques to identify the tissue properties. This paper describes a hand-held probe with a combined vibration source and ultrasound transducer. The design uses a vibration absorption system to counter-balance the reaction forces from contact with the tissue. Simulations and experiments show a high level of vibration absorption. The first elastograms from the probe are also shown.

Hassan Rivaz, Robert Rohling
Real-Time Quality Control of Tracked Ultrasound

The overwhelming majority of intra-operative hazard situations in tracked ultrasound (US) systems are attributed to failure of registration between tracking and imaging coordinate frames. We introduce a novel methodology for real-time in-vivo quality control of tracked US systems, in order to capture registration failures during the clinical procedure. In effect, we dynamically recalibrate the tracked US system for rotation, scale factor, and in-plane position offset up to a scale factor. We detect any unexpected change in these parameters through capturing discrepancies in the resulting calibration matrix, thereby assuring quality (accuracy and consistency) of the tracked system. No phantom is used for the recalibration. We perform the task of quality control in the background, transparently to the clinical user while the subject is being scanned. We present the concept, mathematical formulation, and experimental evaluation in-vitro. This new method can play an important role in guaranteeing accurate, consistent, and reliable performance of tracked ultrasound.

Emad M. Boctor, Iulian Iordachita, Gabor Fichtinger, Gregory D. Hager
Fully Truncated Cone-Beam Reconstruction on Pi Lines Using Prior CT

C-arms are well suited for obtaining cone-beam projections intra-operatively. Due to the compact size of the detector used, the data are usually truncated within the field of view. As a result, direct application of a standard cone-beam reconstruction algorithm gives rise to undesirable artifacts and incorrect values in the reconstructed image volume. When prior information such as a pre-operative CT scan is available, fully truncated cone-beam projections can be used to recover the change within a small region of interest without such artifacts. A method for integrating prior CT is developed using the concept of pi-lines and tested on real flat-panel and simulated cone-beam data.

Krishnakumar Ramamurthi, Norbert Strobel, Rebecca Fahrig, Jerry L. Prince
C-arm Calibration – Is it Really Necessary?

C-arm fluoroscopy is modelled as a perspective projection, the parameters of which are estimated through a calibration procedure. It has been universally accepted that precise intra-procedural calibration is a prerequisite for accurate quantitative C-arm fluoroscopy guidance. Calibration, however, significantly adds to system complexity, which is a major impediment to clinical practice. We challenge the status quo by questioning the assumption that precise intra-procedural calibration is really necessary. We derived theoretical bounds for the sensitivity of 3D measurements to mis-calibration. Experimental results corroborated the theory in that mis-calibration in the focal spot by as much as 50

mm

still allows for tracking with an accuracy of 0.5

mm

in translation and 0.65

o

in rotation, and such mis-calibration does not impose any additional error on the reconstruction of small objects.

Ameet Jain, Ryan Kon, Yu Zhou, Gabor Fichtinger
Laser Needle Guide for the Sonic Flashlight

We have extended the real-time tomographic reflection display of the Sonic Flashlight to a laser guidance system that aims to improve safety and accuracy of needle insertion, especially for deep procedures. This guidance system is fundamentally different from others currently available. Two low-intensity lasers are mounted on opposite sides of a needle aimed parallel to the needle. The needle is placed against a notch in the Sonic Flashlight mirror such that the laser beams reflect off the mirror to create bright red spots on the flat panel display. Due to diffuse reflection from these spots, the virtual image created by the flat panel display contains the spots, identifying the projected destination of the needle at its actual location in the tissue. We have implemented our design and validated its performance, identifying several areas for potential improvement.

David Wang, Bing Wu, George Stetten
Differential Fly-Throughs (DFT): A General Framework for Computing Flight Paths

In this paper, we propose a new variational framework based on distance transform and gradient vector flow, to compute flight paths through tubular and non-tubular structures, for virtual endoscopy. The proposed framework propagates two wave fronts of different speeds from a point source voxel, which belongs to the medial curves of the anatomical structure. The first wave traverses the 3D structure with a moderate speed that is a function of the distance field to extract its topology, while the second wave propagates with a higher speed that is a function of the magnitude of the gradient vector flow to extract the flight paths. The motion of the fronts are governed by a nonlinear partial equation, whose solution is computed efficiently using the higher accuracy fast marching level set method (HAFMM). The framework is robust, fully automatic, and computes flight paths that are centered, connected, thin, and less sensitive to boundary noise. We have validated the robustness of the proposed method both quantitatively and qualitatively against synthetic and clinical datasets.

M. Sabry Hassouna, Aly A. Farag, Robert Falk
Panoramic Views for Virtual Endoscopy

This paper describes a panoramic projection designed to increase the surface visibility during virtual endoscopies. The proposed projection renders five faces of a cubic viewing space into the plane in a continuous fashion. Using this real-time and interactive visualization technique as a screening method for colon cancer could lead to significantly shorter evaluation time. It avoids having to fly through the colon in both directions and prevents the occlusion of potential polyps behind haustral folds.

Bernhard Geiger, Christophe Chefd’hotel, Sandra Sudarsky

Computer Assisted Diagnosis

Toward Automatic Computer Aided Dental X-ray Analysis Using Level Set Method

A Computer Aided Dental X-rays Analysis (CADXA) framework is proposed to semi-automatically detect areas of bone loss and root decay in digital dental X-rays. In this framework, first, a new proposed competitive coupled level set method is proposed to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical environment, the segmentation stage uses a trained support vector machine (SVM) classifier to provide initial contours. Then, based on the segmentation results, an analysis scheme is applied. First, the scheme builds an uncertainty map from which those areas with bone loss will be automatically detected. Secondly, the scheme employs a method based on the SVM and the average intensity profile to isolate the teeth and detect root decay. Experimental results show that our proposed framework is able to automatically detect the areas of bone loss and, when given the orientation of the teeth, it is able to automatically detect the root decay with a seriousness level marked for diagnosis.

Shuo Li, Thomas Fevens, Adam Krzyżak, Chao Jin, Song Li
Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data

Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data analysis methods. In this paper, we contribute a comprehensive framework for spatial and temporal exploration of fMRI data, and apply it to a challenging case study: separating drug addicted subjects from healthy non-drug-using controls. To our knowledge, this is the first time that learning on fMRI data is performed explicitly on temporal information for classification in such applications. Experimental results demonstrate that, by selecting discriminative features, group classification can be successfully performed on our case study although training data are exceptionally high dimensional, sparse and noisy fMRI sequences. The classification performance can be significantly improved by incorporating temporal information into machine learning. Both statistical and neuroscientific validation of the method’s generalization ability are provided. We demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies, facilitates deduction about the behavioral probes from the brain activation data, thus providing a valid tool that incorporates objective brain imaging data into clinical classification of psychopathologies and identification of genetic vulnerabilities.

Lei Zhang, Dimitris Samaras, Dardo Tomasi, Nelly Alia-Klein, Lisa Cottone, Andreana Leskovjan, Nora Volkow, Rita Goldstein
Model-Based Analysis of Local Shape for Lesion Detection in CT Scans

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.

Paulo R. S. Mendonça, Rahul Bhotika, Saad A. Sirohey, Wesley D. Turner, James V. Miller, Ricardo S. Avila
Development of a Navigation-Based CAD System for Colon

We propose a navigation-based computer aided diagnosis (CAD) system for the colon. When diagnosing the colon using virtual colonoscopy (VC), a physician makes a diagnosis by navigating (flying-through) the colon. However, the viewpoints and the viewing directions must be changed many times because the colon is a very long and winding organ with many folds. This is a time-consuming task for physicians. We propose a new

navigation-based

CAD system for the colon providing virtual unfolded (VU) views, which enables physicians to observe a large area of the colonic wall at a glance. This system generates VU, VC, and CT slice views that are perfectly synchronized. Polyp candidates, which are detected automatically, are overlaid on them. We applied the system to abdominal CT images. The experimental results showed that the system effectively generates VU views for observing colon regions.

Masahiro Oda, Takayuki Kitasaka, Yuichiro Hayashi, Kensaku Mori, Yasuhito Suenaga, Jun-ichiro Toriwaki
A Prediction Framework for Cardiac Resynchronization Therapy Via 4D Cardiac Motion Analysis

We propose a novel framework to predict pacing sites in the left ventricle (LV) of a heart and its result can be used to assist pacemaker implantation and programming in cardiac resynchronization therapy (CRT), a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits. In this work, the spherical harmonic (SPHARM) description is employed to model the ventricular surfaces and a novel SPHARM-based surface correspondence approach is proposed to capture the ventricular wall motion. A hierarchical agglomerative clustering technique is applied to the time series of regional wall thickness to identify candidate pacing sites. Using clinical MRI data in our experiments, we demonstrate that the proposed framework can not only effectively identify suitable pacing sites, but also distinguish patients from normal subjects perfectly to help medical diagnosis and prognosis.

Heng Huang, Li Shen, Rong Zhang, Fillia Makedon, Bruce Hettleman, Justin Pearlman
Segmentation and Size Measurement of Polyps in CT Colonography

Virtual colonoscopy is a relatively new method for the detection of colonic polyps. Their size, which is measured from reformatted CT images, mainly determines diagnosis. We present an automatic method for measuring the polyp size. The method is based on a robust segmentation method that grows a surface patch over the entire polyp surface starting from a seed. Projection of the patch points along the polyp axis yields a 2D point set to which we fit an ellipse. The long axis of the ellipse denotes the size of the polyp. We evaluate our method by comparing the automated size measurement with those of two radiologists using scans of a colon phantom. We give data for inter-observer and intra-observer variability of radiologists and our method as well as the accuracy and precision.

J. J. Dijkers, C. van Wijk, F. M. Vos, J. Florie, Y. C. Nio, H. W. Venema, R. Truyen, L. J. van Vliet
Quantitative Nodule Detection in Low Dose Chest CT Scans: New Template Modeling and Evaluation for CAD System Design

Automatic diagnosis of lung nodules for early detection of lung cancer is the goal of a number of screening studies worldwide. With the improvements in resolution and scanning time of low dose chest CT scanners, nodule detection and identification is continuously improving. In this paper we describe the latest improvements introduced by our group in automatic detection of lung nodules. We introduce a new template for nodule detection using level sets which describes various physical nodules irrespective of shape, size and distribution of gray levels. The template parameters are estimated automatically from the segmented data (after the first two steps of our CAD system for automatic nodule detection) – no a priori learning of the parameters’ density function is needed. We show quantitatively that this template modeling approach drastically reduces the number of false positives in the nodule detection (the third step of our CAD system for automatic nodule detection), thus improving the overall accuracy of CAD systems. We compare the performance of this approach with other approaches in the literature and with respect to human experts. The impact of the new template model includes: 1) flexibility with respect to nodule topology – thus

various

nodules can be detected simultaneously by the

same

technique; 2) automatic parameter estimation of the nodule models using the gray level information of the segmented data; and 3) the ability to provide exhaustive search for all the possible nodules in the scan without excessive processing time – this provides an enhanced accuracy of the CAD system without increase in the overall diagnosis time.

Aly A. Farag, Ayman El-Baz, Georgy Gimel’farb, Mohamed Abou El-Ghar, Tarek Eldiasty
Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer

Recently there has been a great deal of interest in algorithms for constructing low-dimensional feature-space embeddings of high dimensional data sets in order to visualize inter- and intra-class relationships. In this paper we present a novel application of graph embedding in improving the accuracy of supervised classification schemes, especially in cases where object class labels cannot be reliably ascertained. By refining the initial training set of class labels we seek to improve the prior class distributions and thus classification accuracy. We also present a novel way of visualizing the class embeddings which makes it easy to appreciate inter-class relationships and to infer the presence of new classes which were not part of the original classification. We demonstrate the utility of the method in detecting prostatic adenocarcinoma from high-resolution MRI.

Anant Madabhushi, Jianbo Shi, Mark Rosen, John E. Tomaszeweski, Michael D. Feldman
Quantification of Emphysema Severity by Histogram Analysis of CT Scans

Emphysema is characterized by the destruction and over distension of lung tissue, which manifest on high resolution computer tomography (CT) images as regions of low attenuation. Typically, it is diagnosed by clinical symptoms, physical examination, pulmonary function tests, and X-ray and CT imaging. In this paper we discuss a quantitative imaging approach to analyze emphysema which employs low-level segmentations of CT images that partition the data into perceptually relevant regions. We constructed multi-dimensional histograms of feature values computed over the image segmentation. For each region in the segmentation, we derive a rich set of feature measurements. While we can use any combination of physical and geometric features, we found that limiting the scope to two features – the mean attenuation across a region and the region area – is effective. The subject histogram is compared to a set of canonical histograms representative of various stages of emphysema using the Earth Mover’s Distance metric. Disease severity is assigned based on which canonical histogram is most similar to the subject histogram. Experimental results with 81 cases of emphysema at different stages of disease progression show good agreement against the reading of an expert radiologist.

Paulo R. S. Mendonça, Dirk R. Padfield, James C. Ross, James V. Miller, Sandeep Dutta, Sardar Mal Gautham

Cellular and Molecular Image Analysis

Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate Unlabeled Medical Data

In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabeled data to be hand-labeled and incorporates them into the training set to improve learning performance. This approach has great potential to reduce the training expense in many medical image analysis applications. The main contributions lie in a new strategy to combine confidence-rated classifiers learned on different feature sets and a robust way to evaluate the “informativeness” of each unlabeled example. Our framework is applied to the problem of classifying microscopic cell images. The experimental results show that 1) our strategy is more effective than simply multiplying the predicted probabilities, 2) the error rate of high-confidence predictions is much lower than the average error rate, and 3) hand-labeling informative examples with low-confidence predictions improves performance efficiently and the performance difference from hand-labeling all unlabeled data is very small.

Weijun He, Xiaolei Huang, Dimitris Metaxas, Xiaoyou Ying
Mosaicing of Confocal Microscopic In Vivo Soft Tissue Video Sequences

Fibered confocal microscopy allows

in vivo

and

in situ

imaging with cellular resolution. The potentiality of this imaging modality is extended in this work by using video mosaicing techniques. Two novelties are introduced. A robust estimator based on statistics for Riemannian manifolds is developed to find a globally consistent mapping of the input frames to a common coordinate system. A mosaicing framework using an efficient scattered data fitting method is proposed in order to take into account the non-rigid deformations and the irregular sampling implied by

in vivo

fibered confocal microscopy. Results on 50 images of a live mouse colon demonstrate the effectiveness of the proposed method.

Tom Vercauteren, Aymeric Perchant, Xavier Pennec, Nicholas Ayache
Segmentation and 3D Reconstruction of Microtubules in Total Internal Reflection Fluorescence Microscopy (TIRFM)

The interaction of the microtubules with the cell cortex plays numerous critical roles in a cell. For instance, it directs vesicle delivery, and modulates membrane adhesions pivotal for cell movement as well as mitosis. Abnormal function of the microtubules is involved in cancer. An effective method to observe microtubule function adjacent to the cortex is TIRFM. To date most analysis of TIRFM images has been done by visual inspection and manual tracing. In this work we have developed a method to automatically process TIRFM images of microtubules so as to enable high throughput quantitative studies. The microtubules are extracted in terms of consecutive segments. The segments are described via Hamilton-Jacobi equations. Subsequently, the algorithm performs a limited reconstruction of the microtubules in 3D. Last, we evaluate our method with phantom as well as real TIRFM images of living cells.

Stathis Hadjidemetriou, Derek Toomre, James S. Duncan

Physically-Based Modeling

Ligament Strains Predict Knee Motion After Total Joint Replacement
A Kinematic Analysis of The Sigma Knee

A passive forward kinematics knee model was used to predict knee motion of a total joint replacement. Given a joint angle, maps of articular surfaces, and patient-specific ligament properties, this model predicted femorotibial contact locations based on the principle of ligament-strain minimization. The model was validated by physical experiments on a commonly implanted knee prosthesis, showing excellent correspondence between the model and actual physical motion. Results suggest that the knee prosthesis studied required an intact posterior cruciate ligament to induce the desirable roll-back motion, and that a single-bundle model of major knee ligaments generated kinematics similar to that of a multi-bundle ligament model. Implications are that a passive model may predict knee kinematics of a given patient, so it may be possible to optimize the implantation of a prosthesis intraoperatively.

Elvis C. S. Chen, Joel L. Lanovaz, Randy E. Ellis
A Boundary Element-Based Approach to Analysis of LV Deformation

Quantification of left ventricular (LV) deformation from 3D image sequences (4D data) is important for the assessment of myocardial viability, which can have important clinical implications. To date, feature information from either Magnetic Resonance, computed tomographic or echocardiographic image data has been assembled with the help of different interpolative models to estimate LV deformation. These models typically are designed to be computationally efficient (e.g. regularizing strategies using B-splines) or more physically realistic (e.g. finite element approximations to biomechanical models), but rarely incorporate both notions. In this paper, we combine an approach to the extraction and matching of image-derived point features based on local shape properties with a boundary element model. This overall scheme is intended to be both computationally efficient and physically realistic. In order to illustrate this, we compute strains using our method on canine 4D MR image sequences and compare the results to those found from a B-spline-based method (termed extended free-form deformation (EFFD)) and a method based on finite elements (FEM). All results are compared to displacements found using implanted markers, taken to be a gold standard.

Ping Yan, Ning Lin, Albert J. Sinusas, James S. Duncan
Reconstruction of Cerebrospinal Fluid Flow in the Third Ventricle Based on MRI Data

A finite-volume model of the cerebrospinal fluid (CSF) system encompassing the third ventricle and the aqueduct of Sylvius was used to reconstruct CSF velocity and pressure fields based on MRI data. The flow domain geometry was obtained through segmentation of MRI brain anatomy scans. The movement of the domain walls was interpolated from brain motion MRI scans. A constant pressure boundary condition (BC) was specified at the foramina of Monro. A transient velocity BC reconstructed from velocimetric MRI scans was employed at the inferior end of the aqueduct of Sylvius. It could be shown that a combination of MRI scans and computational fluid dynamics (CFD) simulation can be used to reconstruct the flow field in the third ventricle. Pre-interventional knowledge of patient-specific CSF flow has the potential to improve neurosurgical interventions such as shunt placement in case of hydrocephalus.

Vartan Kurtcuoglu, Michaela Soellinger, Paul Summers, Kevin Boomsma, Dimos Poulikakos, Peter Boesiger, Yiannis Ventikos
Schwarz Meets Schwann: Design and Fabrication of Biomorphic Tissue Engineering Scaffolds

Tissue engineering is a discipline at the leading edge of the field of computer assisted intervention. This multidisciplinary engineering science is based on the notion of design and fabrication of scaffolds- porous, three-dimensional

"trellis-like"

biomimetic structures that, on implantation, provide a viable environment to recuperate and regenerate damaged cells. Existing CAD-based approaches produce porous labyrinths with

contra-naturam

straight edges. The biomorphic geometry that mimics the

secundam-naturam

substrate would be one that is continuous through all space, partitioned into two not-necessarily-equal sub-spaces by a non-intersecting, two-sided surface.

Minimal surfaces

are ideal to describe such a space. We present results on the premier attempt in computer controlled fabrication and mechanical characterization of Triply Periodic Minimal Surfaces [TPMS]. This initiative is a significant step to link Schwann’s 1838 cell theory with Schwarz’s discovery of TPMS in 1865 to fabricate the previously elusive optimal biomorphic tissue analogs.

Srinivasan Rajagopalan, Richard A. Robb

Robotics and Intervention I

Automatic Detection and Segmentation of Robot-Assisted Surgical Motions

Robotic surgical systems such as Intuitive Surgical’s da Vinci system provide a rich source of motion and video data from surgical procedures. In principle, this data can be used to evaluate surgical skill, provide surgical training feedback, or document essential aspects of a procedure. If processed online, the data can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. This paper examines our progress at developing techniques for “parsing” raw motion data from a surgical task into a labelled sequence of surgical gestures. Our current techniques have achieved > 90% fully automated recognition rates on 15 datasets.

Henry C. Lin, Izhak Shafran, Todd E. Murphy, Allison M. Okamura, David D. Yuh, Gregory D. Hager
DaVinci Canvas: A Telerobotic Surgical System with Integrated, Robot-Assisted, Laparoscopic Ultrasound Capability

We present daVinci Canvas: a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability. DaVinci Canvas consists of the integration of a rigid laparoscopic ultrasound probe with the daVinci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and the display of registered 2D and 3D ultrasound images. Although we used laparoscopic liver cancer surgery as a focusing application, our broader aim was the development of a versatile system that would be useful for many procedures.

Joshua Leven, Darius Burschka, Rajesh Kumar, Gary Zhang, Steve Blumenkranz, Xiangtian (Donald) Dai, Mike Awad, Gregory D. Hager, Mike Marohn, Mike Choti, Chris Hasser, Russell H. Taylor
Design and Control of In-Vivo Magnetic Microrobots

This paper investigates fundamental design, modeling and control issues related to untethered biomedical microrobots guided inside the human body through external magnetic fields. Immediate application areas for these microrobots include cardiovascular, intraocular and inner-ear diagnosis and surgery. A prototype microrobot and steering system are introduced. Experimental results on fluid drag and magnetization properties of the robots are presented along with an analysis of required magnetic fields for application inside blood vessels and vitreous humor.

K. Berk Yesin, Philipp Exner, Karl Vollmers, Bradley J. Nelson
3D Needle-Tissue Interaction Simulation for Prostate Brachytherapy

This paper presents a needle-tissue interaction model that is a 3D extension of a prior work based on the finite element method. The model is also adapted to accommodate arbitrary meshes so that the anatomy can effectively be meshed using third-party algorithms. Using this model a prostate brachytherapy simulator is designed to help medical residents acquire needle steering skills. This simulation uses a prostate mesh generated from clinical data segmented as contours on parallel slices. Node repositioning and addition, which are methods for achieving needle-tissue coupling, are discussed. In order to achieve real-time haptic rates, computational approaches to these methods are compared. Specifically, the benefit of using the Woodbury formula (matrix inversion lemma) is studied. Our simulation of needle insertion into a prostate is shown to run faster than 1 kHz.

Orcun Goksel, Septimiu E. Salcudean, Simon P. DiMaio, Robert Rohling, James Morris
Development and Application of Functional Databases for Planning Deep-Brain Neurosurgical Procedures

This work presents the development and application of a visualization and navigation system for planning deep-brain neurosurgeries. This system, which incorporates a digitized and segmented brain atlas, an electrophysiological database, and collections of final surgical targets of previous patients, provides assistance for non-rigid registration, navigation, and reconstruction of clinical image data. The fusion of standardized anatomical and functional data, once registered to individual patient images, facilitates the delineation of surgical targets. Our preliminary studies compared the target locations identified by a non-expert using this system with those located by an experienced neurosurgeon using regular technique on 8 patients who had undergone subthalamic nucleus (STN) deep-brain stimulations (DBS). The average displacement between the surgical target locations in both groups was 0.58mm ± 0.49mm, 0.70mm ± 0.37mm, and 0.69mm ± 0.34mm in x, y, and z directions respectively, indicating the capability of accurate surgical target initiation of our system, which has also shown promise in planning and guidance for other stereotactic deep-brain neurosurgical procedures.

Ting Guo, Kirk W. Finnis, Andrew G. Parrent, Terry M. Peters

Medical Image Computing for Clinical Applications

Gaze-Contingent Soft Tissue Deformation Tracking for Minimally Invasive Robotic Surgery

The introduction of surgical robots in Minimally Invasive Surgery (MIS) has allowed enhanced manual dexterity through the use of microprocessor controlled mechanical wrists. Although fully autonomous robots are attractive, both ethical and legal barriers can prohibit their practical use in surgery. The purpose of this paper is to demonstrate that it is possible to use real-time binocular eye tracking for empowering robots with human vision by using knowledge acquired

in situ

. By utilizing the close relationship between the horizontal disparity and the depth perception varying with the viewing distance, it is possible to use ocular vergence for recovering 3D motion and deformation of the soft tissue during MIS procedures. Both phantom and in vivo experiments were carried out to assess the potential frequency limit of the system and its intrinsic depth recovery accuracy. The potential applications of the technique include motion stabilization and intra-operative planning in the presence of large tissue deformation.

George P. Mylonas, Danail Stoyanov, Fani Deligianni, Ara Darzi, Guang-Zhong Yang
Registration and Integration for Fluoroscopy Device Enhancement

We investigated a method, motion compensated integration (MCI), for enhancing stent Contrast-to-Noise Ratio (CNR) such that stent deployment may be more easily assessed. MCI registers fluoroscopic frames on the basis of stent motion and performs pixel-wise integration to reduce noise. Registration is based on marker balls, high contrast interventional devices which guide the clinician in stent placement. It is assumed that stent motion is identical to that of the marker balls. Detecting marker balls and identifying their centroids with a high degree of accuracy is a non-trivial task. To address the required registration accuracy, in this work we examine MCI’s visualization benefit as a function of registration error. We employ adaptive forced choice experiments to quantify human discrimination fidelity. Perception results are contrasted with CNR measurements. For each level of registration inaccuracy investigated, MCI conferred a benefit (

p

<0.05) on stent deployment assessment suggesting the technique is tolerant of modest registration error. We also consider the blurring effect of cardiac motion during the x-ray pulse and select frames for integration as a function of cardiac phase imaged.

James C. Ross, David Langan, Ravi Manjeshwar, John Kaufhold, Joseph Manak, David Wilson
Computer Aided Detection for Low-Dose CT Colonography

The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database. The detection sensitivity and specificity values are evaluated at different dose levels. Starting from the original raw-data (acquired at 55mAs), 5 patient datasets (prone and supine scans) are reconstructed at different dose levels (down to 5mAs), using different kernel filters and slice increments. Although the image quality decreases when lowering the acquisition mAs, all polyps above 6mm are successfully detected even at 15mAs. Accordingly the effective dose can be reduced from 4.93mSv to 1.61mSv, without affecting detection capabilities, particularly important when thinking of population screening.

Gabriel Kiss, Johan Van Cleynenbreugel, Stylianos Drisis, Didier Bielen, Guy Marchal, Paul Suetens
Photo-Realistic Tissue Reflectance Modelling for Minimally Invasive Surgical Simulation

Computer-based simulation is an important tool for surgical skills training and assessment. In general, the degree of realism experienced by the trainees is determined by the visual and biomechanical fidelity of the simulator. In minimally invasive surgery, specular reflections provide an important visual cue for tissue deformation, depth and orientation. This paper describes a novel image-based lighting technique that is particularly suitable for modeling mucous-covered tissue surfaces. We describe how noise functions can be used to control the shape of the specular highlights, and how texture noise is generated and encoded in image-based structure at a pre-processing stage. The proposed technique can be implemented at run-time by using the graphics processor to efficiently attain pixel-level control and photo-realism. The practical value of the technique is assessed with detailed visual scoring and cross comparison experiments by two groups of observers.

Mohamed A. ElHelw, Stella Atkins, Marios Nicolaou, Adrian Chung, Guang-Zhong Yang

Biological Imaging - Simulation and Modeling I

Motion Tracking and Intensity Surface Recovery in Microscopic Nuclear Images

Current techniques for microscopic imaging do not provide necessary spatial and temporal resolutions for real time visualization of the nucleus. Images can only be acquired in time lapse mode, leading to significant loss of information between image frames. Such data, if available, can be extremely helpful in the study of nuclear organization and function. In this paper, we present a gamut of geometric-technique-based approaches for solving the problem. Our techniques, working together, can effectively recover complicated motion and deformation as well as the change of intensity surfaces from pairs of images in a microscopic image sequence, and has low time complexity, particularly desirable by many biological applications where large amount of DNA need to be processed. These techniques are also readily applicable to other types of images for reconstructing motion and intensity surfaces of deformable objects.

Lopamudra Mukherjee, Mingen Lin, Jinhui Xu, Ronald Berezney
Towards Automated Cellular Image Segmentation for RNAi Genome-Wide Screening

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

Xiaobo Zhou, K. -Y. Liu, P. Bradley, N. Perrimon, Stephen TC Wong
Adaptive Spatio-Temporal Restoration for 4D Fluorescence Microscopic Imaging

We present a spatio-temporal filtering method for significantly increasing the signal-to-noise ratio (SNR) in noisy fluorescence microscopic image sequences where small particles have to be tracked from frame to frame. Image sequence restoration is achieved using a statistical approach involving an appropriate on-line window geometry specification. We have applied this method to noisy synthetic and real microscopic image sequences where a large number of small fluorescently labeled vesicles are moving in regions close to the Golgi apparatus. The SNR is shown to be drastically improved and the enhanced vesicles can be segmented. This novel approach can be further exploited for biological studies where the dynamics of small objects of interest have to be analyzed in molecular and sub-cellular bio-imaging.

Jérôme Boulanger, Charles Kervrann, Patrick Bouthemy
Kinematic Geometry of Osteotomies

This paper presents a novel method for defining an osteotomy that can be used to represent all types of osteotomy procedures. In essence, we model an osteotomy as a lower-pair mechanical joint to derive the kinematic geometry of the osteotomy. This method was implemented using a commercially available animation software suite in order to simulate a variety of osteotomy procedures. Two osteotomy procedures are presented for a femoral malunion in order to demonstrate the advantages of our kinematic model in developing optimal osteotomy plans. The benefits of this kinematic model include the ability to evaluate the effects of various kinds of osteotomy and the elimination of potentially error-prone radiographic assessment of deformities.

Erin J. Smith, J. Tim Bryant, Randy E. Ellis
Predictive Camera Tracking for Bronchoscope Simulation with CONDensation

This paper exploits the use of temporal information to minimize the ambiguity of camera motion tracking in bronchoscope simulation. The condensation algorithm (Sequential Monte Carlo) has been used to propagate the probability distribution of the state space. For motion prediction, a second-order auto-regressive model has been used to characterize camera motion in a bounded lumen as encountered in bronchoscope examination. The method caters for multi-modal probability distributions, and experimental results from both phantom and patient data demonstrate a significant improvement in tracking accuracy especially in cases where there is airway deformation and image artefacts.

Fani Deligianni, Adrian Chung, Guang Zhong
Experimental Validation of a 3D Dynamic Finite-Element Model of a Total Knee Replacement

A 3D forward-dynamics model of a total knee replacement was developed using an explicit finite-element package. The model incorporated both a tibiofemoral and a patellofemoral joint and allowed full 6-DOF kinematics for both joints. Simulated quadriceps contraction was used to drive the model. For validation, a unique experimental apparatus was constructed to simulate an open-chain extension motion under quadriceps control. The ligamentous constraints of the MCL and LCL were simulated using tension springs. The kinematics of the tibia and patella were recorded along with the net forces and moments applied to the femur. Several ligament and inertial configurations were simulated. The RMS differences between the experimental data and model predictions across all simulations were excellent for both the kinematics (angles: 0.3 – 1.6°, displacements: 0.1 – 0.8 mm) and kinetics (forces: 5 – 11 N, moments: 0.2 – 0.6 Nm). The validated model will be extended with physiologically realistic ligaments and utilized in surgical planning simulations.

Joel L. Lanovaz, Randy E. Ellis
An In Vitro Patient-Tailored Model of Human Cerebral Artery for Simulating Endovascular Intervention

An in vitro patient-tailored reproduction model of cerebral artery, a hardware platform for simulating endovascular intervention for making diagnoses and surgical trainings is presented. 3-D configuration of vessel lumen is reproduced as vessel model with 13

μ

m modeling resolution, using CT and MRI information. Physical characteristics of cerebral artery, such as elastic modulus and friction coefficient, are also reproduced. We also propose a novel method to visualize stress condition on vessel wall using photoelastic effect. Consequently, it should be helpful for clinical applications, academic researches and other various purposes.

Seiichi Ikeda, Fumihito Arai, Toshio Fukuda, Makoto Negoro, Keiko Irie, Ikuo Takahashi
Backmatter
Metadaten
Titel
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005
herausgegeben von
James S. Duncan
Guido Gerig
Copyright-Jahr
2005
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-32094-4
Print ISBN
978-3-540-29327-9
DOI
https://doi.org/10.1007/11566465