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

Abstract

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 atlasbased 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.

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

“…9 To facilitate time-efficient, reproducible, and accurate lesion-load detection, many algorithms have been proposed for fully automated computer-assistive solutions. 3,18 These methods use different principles, including intensity-gradient features, 19 intensity thresholding, 20 intensity-histogram modeling of expected tissue classes, [21][22][23] fuzzy connectedness, 24 identification of nearest neighbors in a feature space, 25,26 or a combination of these. Methods such as Bayesian inference, expectation maximization, support-vector machines, k-nearest neighbor majority voting, and artificial neural networks are algorithmic approaches used to op- Comparing the number of study pairs improved with demyelinating lesions detected by both readers when using the newly developed assistive software to the issued radiology report.…”

Population-Wide Model-Free Quantification of Blood-Brain-Barrier Dynamics in Multiple Sclerosis

“…The 10 subjects analyzed in this paper were scanned over 58 to 155 minutes, and between 13 and 67 volumes were acquired during a single scan. The solid black contours in Figure 2 are the reconstructed in-slice boundaries of the lesions obtained using our Lesion-TOADS automatic segmentation algorithm [Shiee et al, 2010]. Most of the delineated lesions had been present on previous scans of the same subject and did not enhance with contrast.…”

Identification of Structures in Medical Images

“…In order to capture the extraordinary morphological variability of the human brain, a number of automatic methods for identification and analysis of structures have been developed for different imaging modalities (Ashburner et al, 2003;Bandyopadhyay, 2011;Bresser et al, 2011;Ishii et al, 2009;Lopes et al, 2008;Ribbens et al, 2010;Rousset et al, 2007;Shiee et al, 2010;Tu & Bai, 2010;Yi et al, 2009;Zhang et al, 2011).…”