基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络

doi:10.16451/j.cnki.issn1003-6059.202104010

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摘要针对肝脏组织病理图像分割中存在的正常组织和异常组织过渡区域较难分割和空洞较多的问题,设计基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络.在编码器中提取融合多尺度特征,改善正常组织和异常组织过渡区域的分割效果.同时利用注意力机制对空间维度和通道维度进行相关性建模,获得每个像素类内响应和通道间的依赖关系,缓解肝脏组织病理图像空洞较多对网络学习带来的影响.实验表明文中网络可较快速准确分割肝脏组织病理图像损伤区域.

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关键词 ：肝脏组织病理图像, 语义分割, 注意力机制, 多尺度特征提取融合

Abstract：To address the problem of difficult segmentation and many voids in the transition regions of normal and abnormal tissues in liver histopathology images segmentation, a semantic segmentation network of pathological images of liver tissue based on multi-scale feature and attention mechanism is proposed. The fused multi-scale features are extracted in the encoder to improve the segmentation of the transition regions between normal and abnormal tissues. The attention mechanism is employed to model the correlation between spatial dimension and channel dimension to obtain the response of each pixel within each class as well as the dependency between channels, and the impact of many voids in liver histopathology images on the network learning is alleviated. Experiments demonstrate that the proposed network can segment the damaged regions of liver histopathology images more quickly and accurately.

Key words： Pathological Image of Liver Tissue Semantic Segmentation Attention Mechanism Extraction and Fusion of Multi-scale Feature

收稿日期: 2020-06-01

ZTFLH:

TP 391

基金资助:国家自然科学基金重大科研仪器研制项目(No.81727802)、陕西省榆林市科技计划项目(No.CXY-2020-017)、融合视觉感认知的人机协同影像辅助关键技术研究(No.62073260)资助

通讯作者: 卜起荣,博士,副教授,主要研究方向为计算机视觉、医学图像处理、机器学习.E-mail:boqirong@nwu.edu.cn.

作者简介: 张墺琦,硕士研究生,主要研究方向为医学图像处理、机器学习、深度学习.E-mail:zhangaoqi2018@yeah.net. 亢宇鑫,博士研究生,主要方向研究为医学图像处理、机器学习、深度学习.E-mail:yuxinkang@stumail.nwu.edu.cn. 武卓越,硕士研究生,主要研究方向为计算机视觉、机器学习.E-mail:buz2559@dingtalk.com. 崔磊,博士,讲师,主要研究方向为计算机视觉、医学图像处理、深度学习.E-mail:leicui@nwu.edu.cn.

引用本文:

张墺琦, 亢宇鑫, 武卓越, 崔磊, 卜起荣. 基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络[J]. 模式识别与人工智能, 2021, 34(4): 375-384. ZHANG Aoqi, KANG Yuxin, WU Zhuoyue, CUI Lei, BU Qirong. Semantic Segmentation Network of Pathological Images of Liver Tissue Based on Multi-scale Feature and Attention Mechanism. , 2021, 34(4): 375-384.

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