A novel framework for segmentation of deep brain structures based on Markov dependence tree

Abstract

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. In the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average.