OCELOT 2023: Cell detection from cell–tissue interaction challenge

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
JaeWoong Shin , Jeongun Ryu , Aaron Valero Puche , Jinhee Lee , Biagio Brattoli , Wonkyung Jung , Soo Ick Cho , Kyunghyun Paeng , Chan-Young Ock , Donggeun Yoo , Zhaoyang Li , Wangkai Li , Huayu Mai , Joshua Millward , Zhen He , Aiden Nibali , Lydia Anette Schoenpflug , Viktor Hendrik Koelzer , Xu Shuoyu , Ji Zheng , Sérgio Pereira
{"title":"OCELOT 2023: Cell detection from cell–tissue interaction challenge","authors":"JaeWoong Shin ,&nbsp;Jeongun Ryu ,&nbsp;Aaron Valero Puche ,&nbsp;Jinhee Lee ,&nbsp;Biagio Brattoli ,&nbsp;Wonkyung Jung ,&nbsp;Soo Ick Cho ,&nbsp;Kyunghyun Paeng ,&nbsp;Chan-Young Ock ,&nbsp;Donggeun Yoo ,&nbsp;Zhaoyang Li ,&nbsp;Wangkai Li ,&nbsp;Huayu Mai ,&nbsp;Joshua Millward ,&nbsp;Zhen He ,&nbsp;Aiden Nibali ,&nbsp;Lydia Anette Schoenpflug ,&nbsp;Viktor Hendrik Koelzer ,&nbsp;Xu Shuoyu ,&nbsp;Ji Zheng ,&nbsp;Sérgio Pereira","doi":"10.1016/j.media.2025.103751","DOIUrl":null,"url":null,"abstract":"<div><div>Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. However, existing deep learning-based cell detection models struggle to replicate these behaviors and learn the interdependent semantics between structures at different magnifications. A key barrier in the field is the lack of datasets with multi-scale overlapping cell and tissue annotations. The OCELOT 2023 challenge was initiated to gather insights from the community to validate the hypothesis that understanding cell and tissue (cell–tissue) interactions is crucial for achieving human-level performance, and to accelerate the research in this field. The challenge dataset includes overlapping cell detection and tissue segmentation annotations from six organs, comprising 673 pairs sourced from 306 The Cancer Genome Atlas (TCGA) Whole-Slide Images with hematoxylin and eosin staining, divided into training, validation, and test subsets. Participants presented models that significantly enhanced the understanding of cell–tissue relationships. Top entries achieved up to a 7.99 increase in F1-score on the test set compared to the baseline cell-only model that did not incorporate cell–tissue relationships. This is a substantial improvement in performance over traditional cell-only detection methods, demonstrating the need for incorporating multi-scale semantics into the models. This paper provides a comparative analysis of the methods used by participants, highlighting innovative strategies implemented in the OCELOT 2023 challenge.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103751"},"PeriodicalIF":11.8000,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image analysis","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361841525002981","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. However, existing deep learning-based cell detection models struggle to replicate these behaviors and learn the interdependent semantics between structures at different magnifications. A key barrier in the field is the lack of datasets with multi-scale overlapping cell and tissue annotations. The OCELOT 2023 challenge was initiated to gather insights from the community to validate the hypothesis that understanding cell and tissue (cell–tissue) interactions is crucial for achieving human-level performance, and to accelerate the research in this field. The challenge dataset includes overlapping cell detection and tissue segmentation annotations from six organs, comprising 673 pairs sourced from 306 The Cancer Genome Atlas (TCGA) Whole-Slide Images with hematoxylin and eosin staining, divided into training, validation, and test subsets. Participants presented models that significantly enhanced the understanding of cell–tissue relationships. Top entries achieved up to a 7.99 increase in F1-score on the test set compared to the baseline cell-only model that did not incorporate cell–tissue relationships. This is a substantial improvement in performance over traditional cell-only detection methods, demonstrating the need for incorporating multi-scale semantics into the models. This paper provides a comparative analysis of the methods used by participants, highlighting innovative strategies implemented in the OCELOT 2023 challenge.
OCELOT 2023:细胞组织相互作用挑战的细胞检测。
病理学家在检查全片图像时,通常在不同的放大倍数之间交替,使他们能够评估广泛的组织形态和复杂的细胞细节,以形成全面的诊断。然而,现有的基于深度学习的细胞检测模型很难复制这些行为,并在不同的放大倍数下学习结构之间的相互依存语义。该领域的一个关键障碍是缺乏具有多尺度重叠细胞和组织注释的数据集。OCELOT 2023挑战赛的发起是为了收集来自社区的见解,以验证理解细胞和组织(细胞-组织)相互作用对于实现人类水平的性能至关重要的假设,并加速该领域的研究。挑战数据集包括来自6个器官的重叠细胞检测和组织分割注释,包括来自306张癌症基因组图谱(TCGA)的苏木精和伊红染色的全切片图像的673对,分为训练、验证和测试亚组。参与者提出的模型显著增强了对细胞组织关系的理解。与不包含细胞组织关系的基线细胞模型相比,顶级条目在测试集中的f1分数提高了7.99。与传统的仅细胞检测方法相比,这是性能上的重大改进,证明了将多尺度语义纳入模型的必要性。本文对参与者使用的方法进行了比较分析,重点介绍了OCELOT 2023挑战中实施的创新策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信