A Dense-Gated U-Net for Brain Lesion Segmentation

Abstract

Brain lesion segmentation plays a crucial role in diagnosis and monitoring of disease progression. DenseNets have been widely used for medical image segmentation, but much redundancy arises in dense-connected feature maps and the training process becomes harder. In this paper, we address the brain lesion segmentation task by proposing a Dense-Gated U-Net (DGNet), which is a hybrid of Dense-gated blocks and U-Net. The main contribution lies in the dense-gated blocks that explicitly model dependencies among concatenated layers and alleviate redundancy. Based on dense-gated blocks, DGNet can achieve weighted concatenation and suppress useless features. Extensive experiments on MICCAI BraTS 2018 challenge and our collected intracranial hemorrhage dataset demonstrate that our approach outperforms a powerful backbone model and other state-of-the-art methods.