Segmentation of Brain Lesions Using Posterior Distributions Learned by Subspace-assisted Generative Model
Huixiang Zhuang, Yue Guan, Yijie Ding, Chang Xu, Yuhao Ma, Ziyu Meng, Ruihao Liu, Zhi‐Pei Liang, Yao Li · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Motivation: Deep learning shows great potential for brain lesion segmentation but poor generalization (due to limited training data) could lead to false positives. Goal(s): Our goal was to improve the segmentation accuracy by learning target-specific posterior distributions. Approach: We proposed a new Bayesian brain lesion segmentation method, leveraging posterior distributions learning, including both posterior normal and lesion distributions, through a subspace-assisted deep generative model. Results: The proposed method achieved significantly improved segmentation performance across multiple public datasets with stroke, tumor, and multiple sclerosis lesions, in comparison with the state-of-the-art methods. Impact: The proposed method significantly improved accuracy and robustness of lesions segmentation in brain MR images, which may provide a useful tool for brain lesion delineation in image processing and clinical applications.