AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.

Autor: Li W; Department of Computer Science, Johns Hopkins University, United States of America., Qu C; Department of Computer Science, Johns Hopkins University, United States of America., Chen X; Department of Bioengineering, University of Illinois Urbana-Champaign, United States of America., Bassi PRAS; Department of Computer Science, Johns Hopkins University, United States of America; Alma Mater Studiorum - University of Bologna, Italy; Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia, Italy., Shi Y; LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Lai Y; Department of Computer Science, Johns Hopkins University, United States of America; Department of Computer Science, Southeast University, China., Yu Q; Department of Radiology, Southeast University Zhongda Hospital, China., Xue H; Department of Medical Oncology, The First Hospital of China Medical University, China., Chen Y; Department of Computer Science, Johns Hopkins University, United States of America., Lin X; The Second Clinical College, China Medical University, China., Tang Y; The Second Clinical College, China Medical University, China., Cao Y; The Second Clinical College, China Medical University, China., Han H; The Second Clinical College, China Medical University, China., Zhang Z; Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics, Johns Hopkins University, United States of America., Liu J; Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics, Johns Hopkins University, United States of America., Zhang T; Department of Computer Science, Johns Hopkins University, United States of America., Ma Y; Center of Reproductive Medicine, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, China., Wang J; Radiology Department, the First Affiliated Hospital, School of Medicine, Zhejiang University, China., Zhang G; Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, China; Shandong Engineering Research Center of Health Management, China; Shandong Institute of Health Management, China., Yuille A; Department of Computer Science, Johns Hopkins University, United States of America., Zhou Z; Department of Computer Science, Johns Hopkins University, United States of America. Electronic address: zzhou82@jh.edu.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Oct; Vol. 97, pp. 103285. Date of Electronic Publication: 2024 Jul 30.
DOI: 10.1016/j.media.2024.103285
Abstrakt: We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms-the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Zongwei Zhou reports financial support was provided by Johns Hopkins University. Zongwei Zhou reports a relationship with Johns Hopkins University that includes: employment and funding grants. If there are other authors, they 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 © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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