Junde Wu, Yu Zhang, Huihui Fang, Lixin Duan, Mingkui Tan, Weihua Yang, Chunhui Wang, Huiying Liu, Yueming Jin, Yanwu Xu
Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty...
April 26, 2024: IEEE Transactions on Medical Imaging