Correcting and Reweighting False Label Masks in Brain Tumor Segmentation.

PURPOSE Recently, brain tumor segmentation has made important progress. How-ever, the quality of manual labels plays an important role in the performance, while in practice, it could vary greatly and in turn could substantially mislead the learning process and decrease the accuracy. We need to design a mechanism to combine label correction and sample reweighting to improve the effectiveness of brain tumor segmentation. METHODS We propose a novel sample reweighting and label refinement method, and a novel 3D generative adversarial network (GAN) is introduced to combine these two models into an united framework. RESULTS Extensive experiments on the BraTS19 dataset have demonstrated that our approach obtains competitive results when compared with other state-of-the-art approaches when handling the false labels in brain tumor segmentation. CONCLUSIONS The 3D GAN-based approach is an effective approach to handle false label masks by simultaneously applying label correction and sample reweighting. Our method is robust to variations in tumor shape and background clutter.