A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

Automated summary

This study proposes a hybrid-attention nested UNet (Han-Net) model to address the challenge of nuclear segmentation in histopathological images. By combining a hybrid nested U-shaped network and a hybrid attention block, Han-Net extracts discriminative features and effectively segments various types of nuclei. The proposed model achieves state-of-the-art performance in a publicly available multi-organ dataset.