Multi-atlas segmentation and correction model with level set formulation for 3D brain MR images

We present an efficient multi-atlas segmentation and correction model with level set formulation for 3D brain MR images in this paper. We define a new energy functional by combining a weighted label fusion term, a bias field based image information fitting term and a regularization term together. More image information is taken into consideration in the new image data term to substantially improve the segmentation accuracy, especially when serious inhomogeneity and bias field exist in regions of interest in MR images. We introduce a spatially weight function and incorporate it into the label fusion term to increase the robustness of our segmentation algorithm to atlases with different registration accuracy. The new energy functional is in the form of L1 regularization problems, and we minimize it with the split Bregman method to ensure the segmentation efficiency. We apply the proposed model to segment six tissues in 3D brain MR images, including the amygdala, caudate, hippocampus, pallidum, putamen and thalamus. Experimental results have shown that our model can segment regions of interest accurately and eliminate bias field simultaneously. Quantitative comparisons with related methods have demonstrated the superiority of our model in terms of accuracy, efficiency and robustness. (C) 2019 The Authors. Published by Elsevier Ltd.