DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Autor: Da Q; Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Huang X; SenseTime Research, Shanghai, China., Li Z; School of Software Engineering, Xi'an Jiao Tong University, Xi'an, China., Zuo Y; Shanghai Histo Pathology Diagnostic Center, Shanghai, China., Zhang C; SenseTime Research, Shanghai, China., Liu J; Shanghai Histo Pathology Diagnostic Center, Shanghai, China., Chen W; SenseTime Research, Shanghai, China., Li J; SenseTime Research, Shanghai, China., Xu D; School of Software Engineering, Xi'an Jiao Tong University, Xi'an, China., Hu Z; SenseTime Research, Shanghai, China., Yi H; Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Guo Y; Shanghai Histo Pathology Diagnostic Center, Shanghai, China., Wang Z; Department of Pathology, Xijing Hospital, Fourth Military Medical University, Xi'an, China., Chen L; Department of Pathology, Xijing Hospital, Fourth Military Medical University, Xi'an, China., Zhang L; Shanghai Songjiang District Central Hospital, Shanghai, China., He X; National Telemedicine Center of China, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou, China., Zhang X; Shanghai Jiao Tong University, Shanghai, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China., Mei K; Center for Data Science, Beijing University of Posts and Telecommunications, Beijing, China., Zhu C; Center for Data Science, Beijing University of Posts and Telecommunications, Beijing, China., Lu W; Computer Vision Institute, Shenzhen University, Shenzhen, China., Shen L; Computer Vision Institute, Shenzhen University, Shenzhen, China., Shi J; Hefei University of Technology, Hefei, China., Li J; Hefei University of Technology, Hefei, China., S S; Indian Institute of Technology Madras, Chennai, India., Krishnamurthi G; Indian Institute of Technology Madras, Chennai, India., Yang J; Shanghai Jiao Tong University, Shanghai, China., Lin T; Shanghai Jiao Tong University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai, China., Song Q; Real Doctor AI Research Centre, Zhejiang University, Hangzhou, China., Liu X; Real Doctor AI Research Centre, Zhejiang University, Hangzhou, China., Graham S; Mathematics for Real-World Systems Centre for Doctoral Training, University of Warwick, UK; Department of Computer Science, University of Warwick, UK., Bashir RMS; Department of Computer Science, University of Warwick, UK., Yang C; Shanghai Jiao Tong University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai, China., Qin S; Shanghai Jiao Tong University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai, China., Tian X; University of Science and Technology of China, Hefei, China., Yin B; iFLYTEK, Hefei, China., Zhao J; National Telemedicine Center of China, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou, China., Metaxas DN; Department of Computer Science, Rutgers University, Piscataway, NJ, USA., Li H; Multimedia Laboratory, The Chinese University of Hong Kong, Hong Kong, China; Centre for Perceptual and Interactive Intelligence (CPII) Ltd, Hong Kong, China., Wang C; Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: wangchaofu@126.com., Zhang S; Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China. Electronic address: zhangshaoting@pjlab.org.cn.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2022 Aug; Vol. 80, pp. 102485. Date of Electronic Publication: 2022 May 24.
DOI: 10.1016/j.media.2022.102485
Abstrakt: Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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