Brain tumor segmentation using out-of-distribution feature from normal MR images

Abundant data of healthy subjects is available and thus it would be helpful if normal data can boost the brain tumor segmentation. We proposed the out-of-distribution (OOD) feature based on the learned distribution of normal data to discriminate abnormal tumors. Once the neural network was trained to synthesize other contrast MR images with normal data, it failed to properly synthesize the tumor tissues. This OOD characteristic was used for the generation of new feature that can help the tumor segmentation. It was verified that the OOD features contributed to an improvement of segmentation through experiments.