Invasive carcinoma segmentation in whole slide images using MS-ResMTUNet
Yiqing Liu,
Huijuan Shi,
Qiming He,
Yuqiu Fu,
Yizhi Wang,
Yonghong He,
Anjia Han,
Tian Guan
Affiliations
Yiqing Liu
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Huijuan Shi
Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
Qiming He
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Yuqiu Fu
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Yizhi Wang
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Yonghong He
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Anjia Han
Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China; Corresponding authors.
Tian Guan
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China; Corresponding authors.
Identifying the invasive cancer area is a crucial step in the automated diagnosis of digital pathology slices of the breast. When examining the pathological sections of patients with invasive ductal carcinoma, several evaluations are required specifically for the invasive cancer area. However, currently there is little work that can effectively distinguish the invasive cancer area from the ductal carcinoma in situ in whole slide images. To address this issue, we propose a novel architecture named ResMTUnet that combines the strengths of vision transformer and CNN, and uses multi-task learning to achieve accurate invasive carcinoma recognition and segmentation in breast cancer. Furthermore, we introduce a multi-scale input model based on ResMTUnet with conditional random field, named MS-ResMTUNet, to perform segmentation on WSIs. Our systematic experimentation has shown that the proposed network outperforms other competitive methods and effectively segments invasive carcinoma regions in WSIs. This lays a solid foundation for subsequent analysis of breast pathological slides in the future. The code is available at: https://github.com/liuyiqing2018/MS-ResMTUNet
