Brain Tumor Segmentation of Multi-Modality MR Images via Triple Intersecting U-Nets

Abstract In this paper, we propose a triple intersecting U-Nets (TIU-Nets) for brain glioma segmentation. First, the proposed TIU-Nets is composed of binary-class segmentation U-Net (BU-Net) and multi-class segmentation U-Net (MU-Net), in which MU-Net reuses multi-resolution features from BU-Net. Second, we introduce a segmentation soft-mask predicted by BU-Net, that is, candidate glioma region is generated by removing most of non-glioma backgrounds, which guides multi-category segmentation of MU-Net in a weighted manner. Third, an edge branch in MU-Net is leveraged to enhance boundary information of glioma substructure, which facilitates to locate glioma true boundaries and improve segmentation accuracy. Finally, we propose a sigmoid-evolution based polarized cross-entropy loss (S-CE) to resolve class unbalance problem, and apply S-CE loss to soft-mask prediction loss in BU-Net, multi-class segmentation loss in MU-Net and edge prediction loss in edge branch. Experimental results have demonstrated that the proposed 2D/3D TIU-Nets achieves a higher segmentation accuracy than corresponding 2D/3D state-of-the-art segmentation methods including FCN, U-Net, SegNet, CRDN, IVD-Net, FCDenseNet, DeepMedic, DMFNet, etc, evaluating on publicly available brain tumor segmentation challenge 2015 (BRATS2015) datasets. To show the universality of the proposed method, we also give a comparison of segmentation performance on BrainWeb dataset.