Improved Brain Lesion Detection by Integrating Subspace-based Deep Learning of Normal Intensity Distributions and Bayesian Hypothesis Testing
Huixiang Zhuang, Yue Guan, Yijie Ding, Yuhao Ma, Yunpeng Zhang, 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
Unsupervised segmentation of brain lesions is desirable in many applications and has been investigated extensively. In this work, we proposed a new method for brain lesion segmentation, which effectively learns the spatial-intensity distribution of normal brain tissues and then treats lesion segmentation as an anomaly detection problem. We overcame the high-dimensional distribution learning problem using a subspace-assisted generative network. With the learned distribution, the anomaly detection problem was solved using Bayesian hypothesis testing. Our method has been validated using simulated and real brain MR images with stroke and tumor lesions, and produced significantly improved results than several state-of-the-art methods.