He Hongliang, Zhang Chi, Chen Jie, Geng Ruizhe, Chen Luyang, Liang Yongsheng, Lu Yanchang, Wu Jihua, Xu Yongjie
School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Peng Cheng Laboratory, Shenzhen, China.
Front Mol Biosci. 2021 Feb 17;8:614174. doi: 10.3389/fmolb.2021.614174. eCollection 2021.
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.
组织病理学图像的细胞核分割是计算机辅助图像分析中的关键步骤。这些组织病理学图像中存在复杂、多样、密集甚至重叠的细胞核，导致细胞核分割成为一项具有挑战性的任务。为了克服这一挑战，本文提出了一种混合注意力嵌套U-Net（Han-Net），它由两个模块组成：一个混合嵌套U形网络（H部分）和一个混合注意力块（A部分）。H部分将嵌套的多深度U形网络和全分辨率密集网络相结合，以捕获更有效的特征。A部分用于探索注意力信息并建立不同像素之间的相关性。通过这两个模块，Han-Net提取出具有判别性的特征，不仅能有效地分割复杂多样细胞核的边界，还能分割小而密集细胞核的边界。在一个公开可用的多器官数据集中的比较表明，与其他模型相比，所提出的模型实现了最先进的性能。
