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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers

herausgegeben von: Alessandro Crimi, Bjoern Menze, Oskar Maier, Mauricio Reyes, Stefan Winzeck, Heinz Handels

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016.

The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.

Inhaltsverzeichnis

Frontmatter

Brain Lesion Image Analysis

Frontmatter

Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting

Abstract

Pathology can have an important impact on MRI analysis. Specifically, white matter hyper-intensities, tumours, infarcts, etc., can influence the results of various image analysis techniques such as segmentation and registration. Several algorithms have been proposed for image inpainting and restoration, mainly in the context of Multiple Sclerosis lesions. These techniques commonly rely on a set of manually segmented pathological regions for inpainting. Rather than relying on prior segmentations for image restoration, we present a combined segmentation and inpainting algorithm for multimodal images. The proposed method is based on an iterative collaboration between two patch-based techniques, PatchMatch and Non-Local Means, where the former is used to estimate the most probable location of the pathological outliers and the latter to gradually fill the segmented areas with the most plausible multimodal texture. We demonstrate that the proposed method is able to automatically restore multimodal intensities in pathological regions within the context of Multiple Sclerosis.

Ferran Prados, M. Jorge Cardoso, Niamh Cawley, Baris Kanber, Olga Ciccarelli, Claudia A. M. Gandini Wheeler-Kingshott, Sébastien Ourselin

Towards a Second Brain Images of Tumours for Evaluation (BITE2) Database

Abstract

One of the main challenges facing members of the medical imaging community is the lack of real clinical cases and ground truth datasets with which to validate new registration, segmentation, and other image processing algorithms. In this work we present a collection of data from tumour patients acquired at the Montreal Neurological Institute and Hospital that will be released as a publicly available dataset to the image processing community. The database is comprised of 9 patient data sets, in its initial release, that consist of a preoperative and postoperative, gadolinium enhanced T1w MRI, pre- and post- resection tracked intra-operative ultrasound slices and volumes, expert tumour segmentations following the BRATS benchmark, and intra-operative ultrasound with/and MRI registration validation target points. This database extends the already widely used BITE database by improving the quality of registration validation and the variety of data being made available. By including addition features such as expert tumour segmentations, the database will appeal to a broader spectrum of image processing researchers and be useful for validating a wider range of techniques for image-guided neurosurgery.

I. J. Gerard, C. Couturier, M. Kersten-Oertel, S. Drouin, D. De Nigris, J. A. Hall, K. Mok, K. Petrecca, T. Arbel, D. L. Collins

Topological Measures of Connectomics for Low Grades Glioma

Abstract

Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectome-based approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process.

In this manuscript, by using graph theoretical methods of segregation and integration, topological structural connectivity is studied comparing patients with low grade glioma to healthy control. These measures suggest that it is possible to quantify the status of patients pre- and post-surgical intervention to evaluate the condition.

Benjamin Amoah, Alessandro Crimi

Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury

Abstract

Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T₁-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.

Emily L. Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C. Giza, Robert F. Asarnow, Paul M. Thompson

An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study in Glioblastoma

Abstract

Glioblastomas are infiltrative and deeply invasive neoplasms characterized by high vascular proliferation and diffuse margins. As a consequence, this lesion presents a high degree of heterogeneity that requires being studied through a multiparametric combination of several imaging sequences. Nowadays few systems are available to perform a relevant multiparametric analysis of this tumour. In this work, we present the study of GBM by means of http://mtsimaging.com, an online platform for the automatic reporting of multiparametric tissue signatures. The platform implements two full automated GBM pipelines: (1) the anatomical pipeline, which involves MRI preprocessing and tumour segmentation; and (2) the hemodynamic MTS pipeline, which adds the quantification of perfusion parameters and a nosologic segmentation map of the vascular habitats of the GBM. A radiologic report summarizes the findings of both analysis and provides volumetric and perfusion statistics of each tissue and habitat of the tumour.

Javier Juan-Albarracín, Elies Fuster-Garcia, Juan M. García-Gómez

A Fast Approach to Automatic Detection of Brain Lesions

Abstract

Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows as \( O\left( {N\,\log N} \right) \) with the number of voxels, the proposed method computes the cross-correlation in \( O\left( N \right) \). We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy.

Subhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj

Brain Tumor Image Segmentation

Frontmatter

Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

Abstract

Tracking the progression of brain tumors is a challenging task, due to the slow growth rate and the combination of different tumor components, such as cysts, enhancing patterns, edema and necrosis. In this paper, we propose a Deep Neural Network based architecture that does automatic segmentation of brain tumor, and focuses on improving accuracy at the edges of these different classes. We show that enhancing the loss function to give more weight to the edge pixels significantly improves the neural network’s accuracy at classifying the boundaries. In the BRATS 2016 challenge, our submission placed third on the task of predicting progression for the complete tumor region.

Ramandeep S. Randhawa, Ankit Modi, Parag Jain, Prashant Warier

Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields

Abstract

Deep learning techniques have been widely adopted for learning task-adaptive features in image segmentation applications, such as brain tumor segmentation. However, most of existing brain tumor segmentation methods based on deep learning are not able to ensure appearance and spatial consistency of segmentation results. In this study we propose a novel brain tumor segmentation method by integrating a Fully Convolutional Neural Network (FCNN) and Conditional Random Fields (CRF), rather than adopting CRF as a post-processing step of the FCNN. We trained our network in three stages based on image patches and slices respectively. We evaluated our method on BRATS 2013 dataset, obtaining the second position on its Challenge dataset and first position on its Leaderboard dataset. Compared with other top ranking methods, our method could achieve competitive performance with only three imaging modalities (Flair, T1c, T2), rather than four (Flair, T1, T1c, T2), which could reduce the cost of data acquisition and storage. Besides, our method could segment brain images slice-by-slice, much faster than the methods patch-by-patch. We also took part in BRATS 2016 and got satisfactory results. As the testing cases in BRATS 2016 are more challenging, we added a manual intervention post-processing system during our participation.

Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yong Fan, Yazhuo Zhang

Brain Tumor Segmentation with Optimized Random Forest

Abstract

In this paper we propose and tune a discriminative model based on Random Forest (RF) to accomplish brain tumor segmentation in multimodal MR images. The objective of tuning is meant to establish the optimal parameter values and the most significant constraints of the discriminative model. During the building of the RF classifier, the algorithm evaluates the importance of variables, the proximities between data instances and the generalized error. These three properties of RF are employed to optimize the segmentation framework. At the beginning the RF is tuned for variable importance evaluation, and after that it is used to optimize the segmentation framework. The framework was tested on unseen test images from BRATS. The results obtained are similar to the best ones presented in previous BRATS Challenges.

László Lefkovits, Szidónia Lefkovits, László Szilágyi

CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

Abstract

This paper extends a previously published brain tumor segmentation method with a dense Conditional Random Field (CRF). Dense CRFs can overcome the shrinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient cases for testing) and compared to a baseline method using a grid-structured CRF. Improved segmentation performance for the complete and enhancing tumor was observed with respect to grid-structured CRFs.

Raphael Meier, Urspeter Knecht, Roland Wiest, Mauricio Reyes

Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation

Abstract

In this paper, a fully convolutional residual neural network (FCR-NN) based on linear identity mappings is implemented for medical image segmentation, employed here in the setting of brain tumors. Inspired by deep residual networks which won the ImageNet ILSVRC 2015 classification challenge, the FCR-NN combines optimization gains from residual identity mappings with a fully convolutional architecture for image segmentation that efficiently accounts for both low- and high-level image features. After training two separate networks, one for the task of whole tumor segmentation and a second for tissue sub-region segmentation, the serial FCR-NN architecture exceeds state-of-the art with complete tumor, core tumor and enhancing tumor validation Dice scores of 0.87, 0.81 and 0.72 respectively. Despite each FCR-NN comprising a complex 22 layer architecture, the fully convolutional design allows for complete segmentation of a tumor volume within 2 s.

Peter D. Chang

Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation

Abstract

Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

Richard McKinley, Rik Wepfer, Tom Gundersen, Franca Wagner, Andrew Chan, Roland Wiest, Mauricio Reyes

Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients

Abstract

This paper extends a previously published brain tumor segmentation methods based on Random Decision Forest (RDF). An iterative approach is used in training the RDF in each iteration some patients are added to the training data using some heuristics approach instead of randomly selected training dataset. Feature extraction and selection were applied to select the most discriminative features for training our Random Decision forest on. The post-processing phase has a morphological filter to deal with misclassification errors. Our method is capable of detecting the tumor and segmenting the different tumorous tissues of the glioma achieving competitive results.

Abdelrahman Ellwaa, Ahmed Hussein, Essam AlNaggar, Mahmoud Zidan, Michael Zaki, Mohamed A. Ismail, Nagia M. Ghanem

DeepMedic for Brain Tumor Segmentation

Abstract

Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.

Konstantinos Kamnitsas, Enzo Ferrante, Sarah Parisot, Christian Ledig, Aditya V. Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

Abstract

This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.

Adrià Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana

Anatomy-Guided Brain Tumor Segmentation and Classification

Abstract

In this paper, we consider the problem of fully automatic brain tumor segmentation in multimodal magnetic resonance images. In contrast to applying classification on entire volume data, which requires heavy load of both computation and memory, we propose a two-stage approach. We first normalize image intensity and segment the whole tumor by utilizing the anatomy structure information. By dilating the initial segmented tumor as the region of interest (ROI), we then employ the random forest classifier on the voxels, which lie in the ROI, for multi-class tumor segmentation. Followed by a novel pathology-guided refinement, some mislabels of random forest can be corrected. We report promising results obtained using BraTS 2015 training dataset.

Bi Song, Chen-Rui Chou, Xiaojing Chen, Albert Huang, Ming-Chang Liu

Lifted Auto-Context Forests for Brain Tumour Segmentation

Abstract

We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.

Loic Le Folgoc, Aditya V. Nori, Siddharth Ancha, Antonio Criminisi

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Abstract

We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6, 7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases.

Ke Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati, Christos Davatzikos

Interactive Semi-automated Method Using Non-negative Matrix Factorization and Level Set Segmentation for the BRATS Challenge

Abstract

The 2016 BRATS includes imaging data on 191 patients diagnosed with low and high grade gliomas. We present a novel method for multimodal brain segmentation, which consists of (1) an automated, accurate and robust method for image segmentation, combined with (2) semi-automated and interactive multimodal labeling. The image segmentation applies Non-negative Matrix Factorization (NMF), a decomposition technique that reduces the dimensionality of the image by extracting its distinct regions. When combined with the level-set method (LSM), NMF-LSM has proven to be an efficient method for image segmentation. Segmentation of the BRATS images by NMF-LSM is computed by the Cheaha supercomputer at the University of Alabama at Birmingham. The segments of each image are ranked by maximal intensity. The interactive labeling software, which identifies the four targets of the challenge, is semi-automated by cross-referencing the normal segments of the brain across modalities.

Dimah Dera, Fabio Raman, Nidhal Bouaynaya, Hassan M. Fathallah-Shaykh

Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries

Abstract

Automated medical image analysis can play an important role in diagnoses and treatment assessment, but integration and interpretation across heterogeneous data sources remain significant challenges. In particular, automated estimation of tumor extent in glioblastoma patients has been challenging given the diversity of tumor shapes and appearance characteristics due to differences in magnetic resonance (MR) imaging acquisition parameters, scanner variations and heterogeneity in tumor biology. With this work, we present an approach for automated tumor segmentation using multimodal MR images. The algorithm considers the variability arising from the intrinsic tumor heterogeneity and segmentation error to derive the tumor boundary and produce an estimate of segmentation error. Using the MICCAI 2015 dataset, a Dice coefficient of 0.74 was obtained for whole tumor, 0.55 for tumor core, and 0.54 for active tumor, achieving above average performance in comparison to other approaches evaluated on the BRATS benchmark.

Edgar A. Rios Piedra, Benjamin M. Ellingson, Ricky K. Taira, Suzie El-Saden, Alex A. T. Bui, William Hsu

Ischemic Stroke Lesion Image Segmentation

Frontmatter

Predicting Stroke Lesion and Clinical Outcome with Random Forests

Abstract

The treatment of ischemic stroke requires fast decisions for which the potentially fatal risks of an intervention have to be weighted against the presumed benefits. Ideally, the treating physician could predict the outcome under different circumstances beforehand and thus make an informed treatment decision. To this end, this article presents two new methods: one for lesion outcome and one for clinical outcome prediction from multispectral magnetic resonance sequences. After extracting tailored image features, a random forest classifier respectively regressor is trained. Both approaches were submitted to the Ischemic Stroke Lesion Segmentation (ISLES) 2017 challenge and obtained a first and third place. The outcome underlines the robustness of our designed features and stresses the approach’s resilience against overfitting when faced with small training datasets.

Oskar Maier, Heinz Handels

Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke

Abstract

We propose an ensemble of deep neural networks for the two tasks of automated prognosis of post-treatment ischemic stroke, as imposed by the ISLES 2016 Challenge. For lesion outcome prediction, we employ an ensemble of three-dimensional multiscale residual U-Net and a fully convolutional network, trained using image patches. In order to handle class imbalance, we devise a multi-step training strategy. For clinical outcome prediction, we combine a convolutional neural network (CNN) and a logistic regression model. To overcome the small sample size and the need for whole brain image, we use the CNN trained using patches as a feature extractor and trained a shallow network based on the extracted features. Our ensemble approach demonstrated an appealing performance on both problems, and is ranked among the top entries in the Challenge.

Youngwon Choi, Yongchan Kwon, Hanbyul Lee, Beom Joon Kim, Myunghee Cho Paik, Joong-Ho Won

Prediction of Ischemic Stroke Lesion and Clinical Outcome in Multi-modal MRI Images Using Random Forests

Abstract

Herein, we present an automated segmentation method for ischemic stroke lesion segmentation in multi-modal MRI images. The method is based on an ensemble learning technique called random forest (RF), which generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-modal MRI images. The segmentation method is validated on both training and testing data, obtained from MICCAI ISLES-2016 challenge dataset. The evaluation of the method is done by performing two tasks: ischemic stroke lesion outcome prediction (Task I) and clinical outcome prediction (Task II). For Task I, the performance of the method is evaluated relative to the manual segmentation, done by the clinical experts. For Task II, the performance of the method is evaluated relative to the 90 days mRS score, provided as ground truths by ISLES-2016 challenge organizers. The experimental results show the robustness of the segmentation method, and that it achieves reasonable accuracy for the prediction of both ischemic stroke lesion and clinical outcome in multi-modal MRI images.

Qaiser Mahmood, A. Basit

Mild Traumatic Brain Injury Outcome Prediction

Frontmatter

Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome

Abstract

Reliable prediction of traumatic brain injury (TBI) outcome using neuroimaging is clinically important, yet, computationally challenging. To tackle this problem, we developed an injury prediction or classification pipeline based on diffusion tensor imaging (DTI) by combining a novel deep learning approach with statistical permutation tests. We first applied a multi-modal deep learning network to individually train a classification model for each DTI measure. Individual results were then combined to allow iterative refinement of the classification via Tract-Based Spatial Statistics (TBSS) permutation tests, where voxel sum of skeletonized significance values served as a classification performance feedback. Our technique combined a high-performance machine learning algorithm with a conventional statistical tool, which provided a flexible and intuitive approach to predict TBI outcome.

Y. Cai, S. Ji

Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods

Abstract

Cognitive impairment has mainly two, non mutually exclusive, etiologies: structural or connectivity lesions. Analogously, we present here a methodology aimed at investigating magnetic resonance imaging (MRI) scans of subject after a traumatic brain injury (TBI) to detect the presence of these heterogeneous lesions and access the information content within. In particular, we use (i) complex network topological features to capture the effect of disease on connectivity and (ii) morphological brain measurements to describe anomalous patterns from a structural perspective. This integrated base of knowledge is then used to emphasize differences arising within a cohort including normal controls and patients labeled as category-I and category-II according to their outcome after TBI. Results suggest that topological measurements provide a suitable measurement to detect category-I subjects, while structural features are effective to distinguish controls from category-II subjects.

R. Bellotti, A. Lombardi, C. Guaragnella, N. Amoroso, A. Tateo, S. Tangaro

Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury

Abstract

We present an unsupervised three-dimensional feature clustering algorithm to gather the mTOP2016 challenge data into 3 groups. We use the brain MR-T1, diffusion tensor fractional anisotropy, and diffusion tensor mean diffusivity images provided by the mTOP2016 competition. A distance-based size constraint method for data clustering is used. The proposed approach achieves 0.267 adjusted rand index and 0.3556 homogeneity score within the 15 labeled subjects, corresponding to 10 correctly classified data items. Based on visual exploration of the data, we believe that a localized analysis of the lesion regions, using the computed tractography data, is a promising direction to pursue.

Po-Yu Kao, Eduardo Rojas, Jefferson W. Chen, Angela Zhang, B. S. Manjunath

Backmatter

Titel: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
herausgegeben von: Alessandro Crimi
Bjoern Menze
Oskar Maier
Mauricio Reyes
Stefan Winzeck
Heinz Handels
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-55524-9
Print ISBN: 978-3-319-55523-2
DOI: https://doi.org/10.1007/978-3-319-55524-9