Multi-Atlas Segmentation of MR Brain Images with Lesions Using Subspace-Assisted-GAN Based Image Recovery

Motivation: Multi-atlas segmentation (MAS) of MR brain images with lesions is of great clinical significance but remains challenging due to registration inaccuracy caused by pathologies. Goal(s): Our goal was to improve the MAS performance of pathological brain images by restoring more accurate normal images form lesion data. Approach: We integrate a novel subspace-assisted generative model into the MAS framework for estimation of subject-specific posterior normative distribution, which can effectively extract a “hypothetical” normal image from the lesion data, thus enhancing the accuracy of lesion segmentation. Results: Our method produced significantly improved results in normal recovery and MAS compared to the state-of-the-art methods. Impact: The proposed method significantly improves the performance of segmentation of MR brain images with lesions, which may provide a useful tool for tissue segmentation in pathological brain images.