nach oben

Neuroinformatics

Erschienen in:

01.12.2011 | Original Article

An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data

verfasst von: Brian B. Avants, Nicholas J. Tustison, Jue Wu, Philip A. Cook, James C. Gee

Erschienen in: Neuroinformatics | Ausgabe 4/2011

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

Vorheriger Artikel Automated Analysis of Fundamental Features of Brain Structures

Nächster Artikel Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Atropos is one of the three Fates from Greek mythology characterized by her dreaded shears used to decide the destiny of each mortal. Also, consistent with the entomological motif of our ANTs, Acherontia atropos is a species of large moth known for the skull-like pattern visible on its thorax.

In the classic 3-tissue segmentation case, each voxel in the brain region is assigned a label of ‘cerebrospinal fluid (csf)’, ‘gray matter (gm)’, or ‘white matter (wm)’.

Using a more expansive definition of U(x),

$$ U(\mathbf{x}) = \sum\limits_{i = 1}^N \left( V_i(x_i) + \beta \sum\limits_{j \in \mathcal{N}_i} V_{ij}( x_i, x_j ) \right) $$

would permit casting the other prior terms inside the definition of U(x) in the form of the external field V _i(x _i) but, for clarity purposes, we consider them separately.

Due to the lack of parameters in the non-parametric approach, it is not technically an EM algorithm (as described in Wells et al. (1996)). However, the same iterative maximization is applicable and is quite robust in practice as evidenced by the number of researchers employing non-parametric models (see the Introduction).

Consider N sites each with a possible K labels for a total of N ^K possible labeling configurations. For large K ≫ 3, exact optimization is even more intractable than for the traditional 3-tissue scenario.

http://www.itk.org/Doxygen/html/classitk_1_1N4MRIBiasFieldCorrectionImageFilter.html

A comprehensive evaluation of N4 reported in Tustison et al. (2010a) used the BrainWeb data set to compare performance with the original N3 algorithm (Sled et al. 1998).

Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26, 839–851.PubMedCrossRef

Aubert-Broche, B., Griffin, M., Pike, G. B., Evans, A. C., & Collins, D. L. (2006). Twenty new digital brain phantoms for creation of validation image data bases. IEEE Transactions on Medical Imaging, 25, 1410–1416.PubMedCrossRef

Avants, B. B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., et al. (2010a). The optimal template effect in hippocampus studies of diseased populations. Neuroimage, 49, 2457–2466.PubMedCrossRef

Avants, B., Klein, A., Tustison, N., Woo, J., & Gee, J. C. (2010b). Evaluation of open-access, automated brain extraction methods on multi-site multi-disorder data. In 16th annual meeting for the Organization of Human Brain Mapping.

Avants, B., Cook, P. A., McMillan, C., Grossman, M., Tustison, N. J., Zheng, Y., et al. (2010c). Sparse unbiased analysis of anatomical variance in longitudinal imaging. In Proceedings of the 13th international conference on medical image computing and computer-assisted intervention (MICCAI) (Vol. 13, pp. 324–331).

Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54, 2033–2044.PubMedCrossRef

Awate, S. P., Tasdizen, T., Foster, N., & Whitaker, R. T. (2006). Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification. Medical Image Analysis, 10, 726–739.PubMedCrossRef

Balafar, M. A., Ramli, A. R., Saripan, M. I., & Mashohor, S. (2010). Review of brain MRI image segmentation methods. Artificial Intelligence Review, 33, 261–274.CrossRef

Ballester, M. A. G., Zisserman, A. P., & Brady, M. (2002). Estimation of the partial volume effect in MRI. Medical Image Analysis, 6, 389–405.CrossRef

Battaglini, M., Smith, S. M., Brogi, S., & Stefano, N. D. (2008). Enhanced brain extraction improves the accuracy of brain atrophy estimation. Neuroimage, 40, 583–589.PubMedCrossRef

Bazin, P. L., & Pham, D. L. (2007). Topology-preserving tissue classification of magnetic resonance brain images. IEEE Transactions on Medical Imaging, 26, 487–496.PubMedCrossRef

Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Royal Statistical Society B, 36, 192–236.

Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Royal Statistical Society, Series B, 48, 259–302.

Bezdek, J. C., Hall, L. O., & Clarke, L. P. (1993). Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20, 1033–1048.PubMedCrossRef

Boyes, R. G., Gunter, J. L., Frost, C., Janke, A. L., Yeatman, T., Hill, D. L. G., et al. (2008). Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage, 39, 1752–1762.PubMedCrossRef

Boykov, Y. Y., & Jolly, M. P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In Proc. eighth IEEE int. conf. computer vision ICCV 2001 (Vol. 1, pp. 105–112).

Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis amd Machine Intelligence, 26, 1124–1137.CrossRef

Chou, Y. Y., Leporã, N., Avedissian, C., Madsen, S. K., Parikshak, N., Hua, X., et al. (2009). Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer’s disease, mild cognitive impairment and elderly controls. Neuroimage, 46, 394–410.PubMedCrossRef

Clarke, L. P., Velthuizen, R. P., Camacho, M. A., Heine, J. J., Vaidyanathan, M., Hall, L. O., et al. (1995). MRI segmentation: Methods and applications. Magnetic Resonance Imaging, 13, 343–368.PubMedCrossRef

Cline, H. E., Lorensen, W. E., Kikinis, R., & Jolesz, F. (1990). Three-dimensional segmentation of MR images of the head using probability and connectivity. Journal of Computer Assisted Tomography, 14, 1037–1045.PubMedCrossRef

Cuadra, M. B., Cammoun, L., Butz, T., Cuisenaire, O., & Thiran, J. P. (2005). Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Transactions on Medical Imaging, 24, 1548–1565.PubMedCrossRef

Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9, 179–194.PubMedCrossRef

de Boer, R., Vrooman, H. A., Ikram, M. A., Vernooij, M. W., Breteler, M. M. B., van der Lugt, A., et al. (2010). Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods. Neuroimage, 51, 1047–1056.PubMedCrossRef

de Bresser, J., Portegies, M. P., Leemans, A., Biessels, G. J., Kappelle, L. J., & Viergever, M. A. (2011). A comparison of MR based segmentation methods for measuring brain atrophy progression. Neuroimage, 54, 760–768.PubMedCrossRef

Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood estimation from incomplete data using the EM algorithms. Journal of the Royal Statistical Society, 39, 1–38.

Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage, 53, 1–15.PubMedCrossRef

Duncan, J. S., Papademetris, X., Yang, J., Jackowski, M., Zeng, X., & Staib, L. H. (2004). Geometric strategies for neuroanatomic analysis from MRI. Neuroimage, 23(Suppl 1), S34–S45.CrossRef

Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 9, 195–207.PubMedCrossRef

Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., et al. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22.PubMedCrossRef

Freeborough, P. A., & Fox, N. C. (1997). The boundary shift integral: An accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Transactions on Medical Imaging, 16, 623–629.PubMedCrossRef

Freeborough, P. A., Fox, N. C., & Kitney, R. I. (1997). Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. Computer Methods and Programs in Biomedicine, 53, 15–25.PubMedCrossRef

Friston, K. J., Frith, C. D., Liddle, P. F., Dolan, R. J., Lammertsma, A. A., & Frackowiak, R. S. (1990). The relationship between global and local changes in PET scans. Journal of Cerebral Blood Flow and Metabolism, 10, 458–466.PubMedCrossRef

Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.CrossRef

Goualher, G. L., Procyk, E., Collins, D. L., Venugopal, R., Barillot, C., & Evans, A. C. (1999). Automated extraction and variability analysis of sulcal neuroanatomy. IEEE Transactions on Medical Imaging, 18, 206–217.PubMedCrossRef

Greenspan, H., Ruf, A., & Goldberger, J. (2006). Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Transactions on Medical Imaging, 25, 1233–1245.PubMedCrossRef

Hammers, A., Allom, R., Koepp, M. J., Free, S. L., Myers, R., Lemieux, L., et al. (2003). Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human Brain Mapping, 19, 224–247.PubMedCrossRef

Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006). Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage, 33, 115–126.PubMedCrossRef

Heckemann, R. A., Keihaninejad, S., Aljabar, P., Rueckert, D., Hajnal, J. V., Hammers, A., et al. (2010). Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage, 51, 221–227.PubMedCrossRef

Held, K., Kops, E. R., Krause, B. J., Wells, W. M., Kikinis, R., & Müller-Gärtner, H. W. (1997). Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging, 16, 878–886.PubMedCrossRef

Julin, P., Melin, T., Andersen, C., Isberg, B., Svensson, L., & Wahlund, L. O. (1997). Reliability of interactive three-dimensional brain volumetry using MP-RAGE magnetic resonance imaging. Psychiatry Research, 76, 41–49.PubMedCrossRef

Kikinis, R., Shenton, M. E., Gerig, G., Martin, J., Anderson, M., Metcalf, D., et al. (1992). Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. Journal of Magnetic Resonance Imaging, 2, 619–629.PubMedCrossRef

Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., & Lundervold, A. (2009). Evaluation of automated brain MR image segmentation and volumetry methods. Human Brain Mapping, 30, 1310–1327.PubMedCrossRef

Klein, A., & Hirsch, J. (2005). Mindboggle: A scatterbrained approach to automate brain labeling. Neuroimage, 24, 261–280.PubMedCrossRef

Leemput, K. V., Maes, F., Vandermeulen, D., & Suetens, P. (1999a). Automated model-based bias field correction of MR images of the brain. IEEE Transactions on Medical Imaging, 18, 885–896.PubMedCrossRef

Leemput, K. V., Maes, F., Vandermeulen, D., & Suetens, P. (1999b). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging, 18, 897–908.PubMedCrossRef

Leemput, K. V., Maes, F., Vandermeulen, D., & Suetens, P. (2003). A unifying framework for partial volume segmentation of brain MR images. IEEE Transactions on Medical Imaging, 22, 105–119.PubMedCrossRef

Li, S. Z. (2001). Markov random field modeling in computer vision. London: Springer.

Lim, K. O., & Pfefferbaum, A. (1989). Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. Journal of Computer Assisted Tomography, 13, 588–593.PubMedCrossRef

Marroquin, J. L., Vemuri, B. C., Botello, S., Calderon, F., & Fernandez-Bouzas, A. (2002). An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Transactions on Medical Imaging, 21, 934–945.PubMedCrossRef

Nakamura, K., & Fisher, E. (2009). Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients. Neuroimage, 44, 769–776.PubMedCrossRef

Noe, A., & Gee, J. C. (2001). Partial volume segmentation of cerebral MRI scans with mixture model clustering. In M. Insana, & R. Leahy (Eds.), Information processing in medical imaging. Lecture notes in computer science (Vol. 2082, pp. 423–430). Berlin: Springer.CrossRef

Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26, 1277–1294.CrossRef

Pappas, T. N. (1992). An adaptive clustering algorithm for image segmentation. IEEE Transactions on Signal Processing, 40, 901–914.CrossRef

Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2, 315–337.PubMedCrossRef

Pieper, S., Lorensen, B., Schroeder, W., & Kikinis, R. (2006). The NA-MIC kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community. In Proceedings of the 3rd IEEE international symposium on biomedical imaging: From nano to macro (Vol. 1, pp. 698–701).

Pohl, K. M., Bouix, S., Nakamura, M., Rohlfing, T., McCarley, R. W., Kikinis, R., et al. (2007). A hierarchical algorithm for MR brain image parcellation. IEEE Transactions on Medical Imaging, 26, 1201–1212.PubMedCrossRef

Pohl, K. M., Fisher, J., Grimson, W. E. L., Kikinis, R., & Wells, W. M. (2006). A Bayesian model for joint segmentation and registration. Neuroimage, 31, 228–239.PubMedCrossRef

Prastawa, M., Gilmore, J. H., Lin, W., & Gerig, G. (2005). Automatic segmentation of MR images of the developing newborn brain. Medical Image Analysis, 9, 457–466.PubMedCrossRef

Ruan, S., Jaggi, C., Xue, J., Fadili, J., & Bloyet, D. (2000). Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE Transactions on Medical Imaging, 19, 1179–1187.PubMedCrossRef

Sanjay-Gopal, S., & Hebert, T. J. (1998). Bayesian pixel classification using spatially variant finite mixtures and the generalized em algorithm. IEEE Transactions on Image Processing, 7, 1014–1028.PubMedCrossRef

Sánchez-Benavides, G., Gómez-Ansón, B., Sainz, A., Vives, Y., Delfino, M., & Peña-Casanova, J. (2010). Manual validation of Freesurfer’s automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer disease subjects. Psychiatry Research, 181, 219–225.PubMedCrossRef

Scherrer, B., Forbes, F., Garbay, C., & Dojat, M. (2009). Distributed local MRF models for tissue and structure brain segmentation. IEEE Transactions on Medical Imaging, 28, 1278–1295.PubMedCrossRef

Shiee, N., Bazin, P. L., Ozturk, A., Reich, D. S., Calabresi, P. A., & Pham, D. L. (2010). A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage, 49, 1524–1535.PubMedCrossRef

Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17, 87–97.PubMedCrossRef

Smith, S. M., Rao, A., Stefano, N. D., Jenkinson, M., Schott, J. M., Matthews, P. M., et al. (2007). Longitudinal and cross-sectional analysis of atrophy in alzheimer’s disease: Cross-validation of BSI, SIENA and SIENAX. Neuroimage, 36, 1200–1206.PubMedCrossRef

Suri, J. S., Singh, S., & Reden, L. (2002). Computer vision and pattern recognition techniques for 2-D and 3-D MR cerebral cortical segmentation (part I): A state-of-the-art review. Pattern Analysis & Applications, 5, 46–76. doi:10.1007/s100440200005.CrossRef

Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., et al. (2010a). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29, 1310–1320.PubMedCrossRef

Tustison, N., Avants, B., Altes, T., de Lange, E., Mugler, J., & Gee, J. (2010b). Automatic segmentation of ventilation defects in hyperpolarized 3He MRI. In Proceedings of the biomedical engineering society annual meeting.

Tustison, N., Avants, B., Siqueira, M., & Gee, J. (2010c). Topological well-composedness and Glamorous Glue: A digital gluing algorithm for topologically constrained front propagation. IEEE Transactions on Image Processing, accepted.

Vannier, M. W., Butterfield, R. L., Jordan, D., Murphy, W. A., Levitt, R. G., & Gado, M. (1985). Multispectral analysis of magnetic resonance images. Radiology, 154, 221–224.PubMed

Viergever, M. A., Maintz, J. B., Niessen, W. J., Noordmans, H. J., Pluim, J. P., Stokking, R., et al. (2001). Registration, segmentation, and visualization of multimodal brain images. Computerized Medical Imaging and Graphics, 25, 147–151.PubMedCrossRef

Weisenfeld, N. I., & Warfield, S. K. (2009). Automatic segmentation of newborn brain MRI. Neuroimage, 47, 564–572.PubMedCrossRef

Wells, W. M., Grimson, W. L., Kikinis, R., & Jolesz, F. A. (1996). Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15, 429–442.PubMedCrossRef

Westlye, L. T., Walhovd, K. B., Dale, A. M., Espeseth, T., Reinvang, I., Raz, N., et al. (2009). Increased sensitivity to effects of normal aging and Alzheimer’s disease on cortical thickness by adjustment for local variability in gray/white contrast: A multi-sample MRI study. Neuroimage, 47, 1545–1557.PubMedCrossRef

Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67–82.CrossRef

Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., et al. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31, 1116–1128.PubMedCrossRef

Zaidi, H., Ruest, T., Schoenahl, F., & Montandon, M. L. (2006). Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET. Neuroimage, 32, 1591–1607.PubMedCrossRef

Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20, 45–57.PubMedCrossRef

Titel: An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data
verfasst von: Brian B. Avants
Nicholas J. Tustison
Jue Wu
Philip A. Cook
James C. Gee
Publikationsdatum: 01.12.2011
Verlag: Springer-Verlag
Erschienen in: Neuroinformatics / Ausgabe 4/2011
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-011-9109-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2011

Neuropsychological Testing and Structural Magnetic Resonance Imaging as Diagnostic Biomarkers Early in the Course of Schizophrenia and Related Psychoses

Automated Analysis of Fundamental Features of Brain Structures

Cerebral Aneurysms Database, a New Free Database for Intracranial Aneurysms

Next Steps in Data Publishing

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

Dataset of Magnetic Resonance Images of Nonepileptic Subjects and Temporal Lobe Epilepsy Patients for Validation of Hippocampal Segmentation Techniques