Author：
Da, Qian
(Da, Qian.)
|
Huang, Xiaodi
(Huang, Xiaodi.)
|
Li, Zhongyu
(Li, Zhongyu.)
|
Zuo, Yanfei
(Zuo, Yanfei.)
|
Zhang, Chenbin
(Zhang, Chenbin.)
|
Liu, Jingxin
(Liu, Jingxin.)
|
Chen, Wen
(Chen, Wen.)
|
Li, Jiahui
(Li, Jiahui.)
|
Xu, Dou
(Xu, Dou.)
|
Hu, Zhiqiang
(Hu, Zhiqiang.)
|
Yi, Hongmei
(Yi, Hongmei.)
|
Guo, Yan
(Guo, Yan.)
|
Wang, Zhe
(Wang, Zhe.)
|
Chen, Ling
(Chen, Ling.)
|
Zhang, Li
(Zhang, Li.)
|
He, Xianying
(He, Xianying.)
|
Zhang, Xiaofan
(Zhang, Xiaofan.)
|
Mei, Ke
(Mei, Ke.)
|
Zhu, Chuang
(Zhu, Chuang.)
|
Lu, Weizeng
(Lu, Weizeng.)
|
Shen, Linlin
(Shen, Linlin.)
|
Shi, Jun
(Shi, Jun.)
|
Li, Jun
(Li, Jun.)
|
S, Sreehari
(S, Sreehari.)
|
Krishnamurthi, Ganapathy
(Krishnamurthi, Ganapathy.)
|
Yang, Jiangcheng
(Yang, Jiangcheng.)
|
Lin, Tiancheng
(Lin, Tiancheng.)
|
Song, Qingyu
(Song, Qingyu.)
|
Liu, Xuechen
(Liu, Xuechen.)
|
Graham, Simon
(Graham, Simon.)
|
Bashir, Raja Muhammad Saad
(Bashir, Raja Muhammad Saad.)
|
Yang, Canqian
(Yang, Canqian.)
|
Qin, Shaofei
(Qin, Shaofei.)
|
Tian, Xinmei
(Tian, Xinmei.)
|
Yin, Baocai
(Yin, Baocai.)
|
Zhao, Jie
(Zhao, Jie.)
|
Metaxas, Dimitris N
(Metaxas, Dimitris N.)
|
Li, Hongsheng
(Li, Hongsheng.)
|
Wang, Chaofu
(Wang, Chaofu.)
|
Zhang, Shaoting
(Zhang, Shaoting.)
Unfold
Abstract：
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. © 2022 Elsevier B.V.
Keyword：
Cells
Computer aided diagnosis
Cytology
Digestive system
Diseases
Image segmentation
Medical imaging
Pathology
Tissue
Reprint Author's Address：
-
[Zhang, S.]Department of Pathology, China;;[Zhang, S.]Department of Pathology, China;;
Classification
461.1 Biomedical Engineering - 461.2 Biological Materials and Tissue Engineering - 461.6 Medicine and Pharmacology - 461.9 Biology - 723.5 Computer Applications - 746 Imaging Techniques
Type
This study was supported by the funding of the Science and Technology Commission Shanghai Municipality ( 19511121400 ), the National Key Research and Development Project of China ( 2020YFC2004800 ), the Science and Technology Project for Innovation Ecosystem Construction of Zhengzhou National Supercomputing Center (201400210400), and the Central Guidance on Local Science and Technology Development Fund of Henan Province. The authors would like to thank the Intel® student Ambassador Program for AI, which provided the necessary computational resources on the Intel® AI DevCloud for running the WSI inference pipeline. The authors are also grateful to the NVIDIA GPU Grant Program for donating Titan-V GPU used in this research. The authors would like to thank Qi Duan, and Shuang Yang from SenseTime Research for their great help in challenging organizations. The authors are thankful to Xiao Mu, Bo Xu, Bin Gu, Zhaoyu Wang, Qiang Li from Shanghai Histo Pathology Diagnostic Center, Weiwei Sun, Minmin Gu, Caijuan Li, Yanqiong Zhang from Shanghai Songjiang District Central Hospital for their work in preparation of the data set.This study was supported by the funding of the Science and Technology Commission Shanghai Municipality (19511121400), the National Key Research and Development Project of China (2020YFC2004800), the Science and Technology Project for Innovation Ecosystem Construction of Zhengzhou National Supercomputing Center (201400210400), and the Central Guidance on Local Science and Technology Development Fund of Henan Province. The authors would like to thank the Intel® student Ambassador Program for AI, which provided the necessary computational resources on the Intel® AI DevCloud for running the WSI inference pipeline. The authors are also grateful to the NVIDIA GPU Grant Program for donating Titan-V GPU used in this research. The authors would like to thank Qi Duan, and Shuang Yang from SenseTime Research for their great help in challenging organizations. The authors are thankful to Xiao Mu, Bo Xu, Bin Gu, Zhaoyu Wang, Qiang Li from Shanghai Histo Pathology Diagnostic Center, Weiwei Sun, Minmin Gu, Caijuan Li, Yanqiong Zhang from Shanghai Songjiang District Central Hospital for their work in preparation of the data set.
Access Number
EI:20222912368033
WOS:000816217100004
Scopus:2-s2.0-85134035713
Corresponding authors email
