AUTHOR=He Hongliang , Zhang Chi , Chen Jie , Geng Ruizhe , Chen Luyang , Liang Yongsheng , Lu Yanchang , Wu Jihua , Xu Yongjie TITLE=A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images JOURNAL=Frontiers in Molecular Biosciences VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2021.614174 DOI=10.3389/fmolb.2021.614174 ISSN=2296-889X ABSTRACT=

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.