个人中心| | 帮助
高级检索
当前位置： 首页 > 详情页

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

| 认领 | 导出 | |

文献详情

资源类型：
Pubmed体系：
作者：
Graham Simon[1,2] * ; Vu Quoc Dang[1,2] * ; Jahanifar Mostafa[1] ; Weigert Martin[3] ; Schmidt Uwe[4] ; Zhang Wenhua[5] ; Zhang Jun[6] ; Yang Sen[7] ; Xiang Jinxi[8] ; Wang Xiyue[9] ; Rumberger Josef Lorenz[10,11,12] ; Baumann Elias[13] ; Hirsch Peter[10,11] ; Liu Lihao[14] ; Hong Chenyang[15] ; Aviles-Rivero Angelica I[14] ; Jain Ayushi[16,17] ; Ahn Heeyoung[18] ; Hong Yiyu[18] ; Azzuni Hussam[19] ; Xu Min[19] ; Yaqub Mohammad[19] ; Blache Marie-Claire[20] ; Piegu Benoit[20] ; Vernay Bertrand[21,22,23,24] ; Scherr Tim[25] ; Bohland Moritz[25] ; Loffler Katharina[25] ; Li Jiachen[26] ; Ying Weiqin[26] ; Wang Chixin[26] ; Snead David[2,27,28] ; Raza Shan E Ahmed[1] ; Minhas Fayyaz[1] ; Rajpoot Nasir M[1,2,27] ;
机构： [1]Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom [2]Histofy Ltd, Birmingham, United Kingdom [3]Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland [4]Independent Researcher, Dresden, Germany [5]The Department of Computer Science, The University of Hong Kong, Hong Kong [6]Tencent AI Lab, Shenzhen, China [7]College of Biomedical Engineering, Sichuan University, Chengdu, China [8]Department of Precision Instruments, Tsinghua University, Beijing, China [9]College of Computer Science, Sichuan University, Chengdu, China [10]Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany [11]Humboldt University of Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany [12]Charité University Medicine, Berlin, Germany [13]University of Bern, Bern, Switzerland [14]Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom [15]Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong [16]Softsensor.ai, Bridgewater, NJ, United States of America [17]PRR.ai, TX, United States of America [18]Department of R&D Center, Arontier Co. Ltd, Seoul, Republic of Korea [19]Computer Vision Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates [20]CNRS, IFCE, INRAE, Université de Tours, PRC, 3780, Nouzilly, France [21]Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France [22]Centre National de la Recherche Scientifique, UMR7104, Illkirch, France [23]Institut National de la Santé et de la Recherche Médicale, INSERM, U1258, Illkirch, France [24]Université de Strasbourg, Strasbourg, France [25]Institute for Automation and Applied Informatics Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany [26]School of software engineering, South China University of Technology, Guangzhou, China [27]Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom [28]Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
出处：
DOI： 10.1016/j.media.2023.103047
ISSN：

关键词： Computational pathology Nuclear recognition Deep learning

摘要：
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

基金：
SG, MJ, DS, SR, FM and NR would like to acknowledge the support from the PathLAKE digital pathology consortium which is funded by the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). FM acknowledges funding from EPSRC, United Kingdom grant EP/W02909X/1.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2024]版：
无
最新[2023]版：
大类 | 1 区 医学
小类 | 1 区 计算机：人工智能 1 区 计算机：跨学科应用 1 区 工程：生物医学 1 区 核医学
第一作者：
第一作者机构： [1]Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom [2]Histofy Ltd, Birmingham, United Kingdom
共同第一作者：
通讯作者：
通讯机构： [1]Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom [2]Histofy Ltd, Birmingham, United Kingdom
推荐引用方式(GB/T 7714)：
Graham Simon,Vu Quoc Dang,Jahanifar Mostafa,et al.CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting[J].Medical Image Analysis.2024,92:103047.doi:10.1016/j.media.2023.103047.
APA：
Graham Simon,Vu Quoc Dang,Jahanifar Mostafa,Weigert Martin,Schmidt Uwe...&Rajpoot Nasir M.(2024).CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.Medical Image Analysis,92,
MLA：
Graham Simon,et al."CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting".Medical Image Analysis 92.(2024):103047

相关文献

[1]Multimodal deep learning approaches for precision oncology: a comprehensive review [2]Dwell locations and dwell time prediction of cervical cancer brachytherapy using 3D U-net [3]A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme [4]A novel deep learning segmentation model for organoid-based drug screening [5]Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer [6]Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study [7]Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies [8]Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application [9]Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma [10]Deep Learning-Based Automatic Segmentation of Brain Tumors in Heterogenous Multi-Center Magnetic Resonance Imaging Sets

资源点击量：53699 今日访问量：0 总访问量：4607 更新日期：2025-02-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持：重庆聚合科技有限公司 地址：成都市人民南路四段55号