ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation

Organ cancer have a high mortality rate. In order to help doctors diagnose and treat organ lesion, an automatic medical image segmentation model is urgently needed as manually segmentation is time-consuming and error-prone. However, automatic segmentation of target organ from medical images is a challenging task because of organ’s uneven and irregular shapes. In this paper, we propose an attention-based nested segmentation network, named ANU-Net. Our proposed network has a deep supervised encoder-decoder architecture and a redesigned dense skip connection. ANU-Net introduces attention mechanism between nested convolutional blocks so that the features extracted at different levels can be merged with a task-related selection. Besides, we redesign a hybrid loss function combining with three kinds of losses to make full use of full resolution feature information. We evaluated proposed model on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset and ISBI 2019 Combined Healthy Abdominal Organ Segmentation (CHAOS) Challenge. ANU-Net achieved very competitive performance for four kinds of medical image segmentation tasks.