Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures

Abstract We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to evaluate whether a brain structure exhibits group level differences as a function of disease in a set of images. Typically, interactive expert guided segmentation has been the method of choice for such applications, but this is tedious for larger datasets common today (> 200 images). To address this problem, the strategy we adopt is to endow an image segmentation algorithm with "advice" encoding some global characteristics of the region(s) we want to extract. This is accomplished by constructing (using expert-segmented images) an epitome of a specific region - as a histogram over a bag of 'words' (e.g., suitable feature descriptors). Now, given such a representation, the problem reduces to segmenting a new 3-D DT-MR image of the brain with additional constraints that enforce consistency between the segmented foreground and the pre-specified histogram (over features). We present combinatorial approximation algorithms to incorporate such domain specific constraints for Markov Random Field (MRF) segmentation. Making use of recent results on concurrent segmentation of multiple images, we derive effective solution strategies for our problem. We describe our main ideas, provide analysis of solution quality, and present promising experimental evidence showing that many structures of interest in Neuroscience can be extracted reliably from 3-D DT-MR brain image volumes using our algorithm.

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@inproceedings{motwani2010,
 title = {Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures},
 author = {Kamiya Motwani and Nagesh Adluru and Chris Hinrichs and Andrew L. Alexander and Vikas Singh},
 booktitle = {Advances in Neural Information Processing Systems 23},
 pages = {1696--1704},
 year = {2010}
}