Subcortical Brain Segmentation Based on Atlas Registration and Linearized Kernel Sparse Representative Classifier

Abstract

Automatic subcortical brain segmentation in a magnetic resonance image (MRI) is crucial to disease diagnosis and various clinical applications. In this paper, we proposed an efficient segmentation method based on atlas registration (AR) and linearized kernel sparse representative classifier (SRC) to segment thalamus, putamen, caudate, pallidum, hippocampus, and amygdala. Many multi-atlas-based segmentation methods (MAS) have been proposed with success. However, the optimization problems in MAS for each voxel cause heavy computational burden. Besides, brain structures in the MR image have the characteristics of small volume, large morphological difference, and blur edge, which make the precise segmentation more difficult. To address these challenges, in the first step of our proposed method, we used AR to estimate subcortical locations in MRI. We constructed a probabilistic atlas for deep structures by AR and then obtained coarse results with initial shapes of structures. Candidate boundary regions were obtained in this stage. Then, in the second step, the kernel SRC was linearized and was applied to refine results around boundaries. The labels of voxels in candidate boundary regions were predicted in this stage, and this refinement was utilized to smooth the boundary. Finally, combining initial shapes and refined boundaries, we obtained a final segmentation of the subcortical brain. The experiments were conducted on IBSR, LPBA40, and SATA MICCAI 2013 challenge datasets. The results showed that our method outperformed other methods including AR, linearized SRC, and deep learning models with mean DSC of 0.843, 0.83, and 0.827, respectively. The time-consumption was relatively lower than the comparison methods. The proposed method has great potential for other segmentation tasks.