Hard-Boundary Attention Network for Nuclei Instance Segmentation

Abstract

Image segmentation plays an important role in medical image analysis, and accurate segmentation of nuclei is especially crucial to clinical diagnosis. However, existing methods fail to segment dense nuclei due to the hard-boundary which has similar texture to nuclear inside. To this end, we propose a Hard-Boundary Attention Network (HBANet) for nuclei instance segmentation. Specifically, we propose a Background Weaken Module (BWM) to weaken the attention of our model to the nucleus background by integrating low-level features into high-level features. To improve the robustness of the model to the hard-boundary of nuclei, we further design a Gradient-based boundary adaptive Strategy (GS) which generates boundary-weakened data for model training in an adversarial manner. We conduct extensive experiments on MoNuSeg and CPM-17 datasets, and experimental results show that our HBANet outperforms the state-of-the-art methods.