A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions

By Navid Shiee, Pierre-Louis Bazin, Arzu Ozturk, Daniel S. Reich, Peter A. Calabresi, Dzung L. Pham. NeuroImage, 2009

346 citations recorded by the source corpus.

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.

Subfields: Biophysics, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ecafc65d094c52300ffa2c9640303c584f4d569b

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In Biophysics

Tatsuya Harada

In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

Citations in the corpus

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging (2013, methodology): “In WML segmentation, the images most widely employed for validation are those from BrainWeb (www.bic.mni.mcgill.ca/ServicesBrainWeb), an online database of synthetic MR images (Cocosco et al., 1997; Collins et al., 1998).”
Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks (2020, methodology): “These approaches have employed stochastic intensity models (Van Leemput et al., 2001), atlas-based registration (Prastawa et al., 2004), clustering (Shiee et al., 2010), content-based retrieval (Weiss et al., 2013), and statistical models based on image registration (Tomas-Fernandez and Warﬁeld,…”
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017, neutral): “A complete description of Lesion-TOADS is available in (Roth et al., 2014).”
Statistical normalization techniques for magnetic resonance imaging (2014, methodology): “For performance assessment within tissue types, TOADS (Bazin and Pham, 2007) was used for segmentation of the brain in the AD studies, and LesionTOADS (Shiee et al., 2010) was used in MS studies.”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “5T) Tomas [94] – – 9v (GE 3T) Shiee [30] 0.”
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation (2016, methodology): “Alternatively, clustering methods have been used to segment healthy and lesion tissue, where lesions are modelled as a separate tissue class [35], [39].”