Unified multi-scale feature abstraction for medical image segmentation

Multi-scale contextual information is effective for pixel-level label prediction, i.e. image segmentation. However, such important information is only partially exploited in the existing methods. In this paper, we propose a new network architecture for unified multi-scale feature abstraction. The proposed network performs multi-scale analysis to the input image by using spatial pyramid pooling to obtain scene context information and abstract multi-scale features hierarchically. In addition, we present a new skip pathways to learn context information by fusing semantically similar features and develop a deep supervision mechanism for outputs in different scales. The proposed mechanisms relieve the gradient vanishing problem and enforce semantic feature learning. We extensively evaluated our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset and demonstrate highly competitive performance with single step operation and lightweight 2D networks.