AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

doi:10.1109/TPAMI.2021.3100536

	AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
	Ma, Jun 1; Zhang, Yao 2,3; Gu, Song 4; Zhu, Cheng 5; Ge, Cheng 6; Zhang, Yichi 7; An, Xingle 8; Wang, Congcong 9,10; Wang, Qiyuan 11; Liu, Xin 12; Cao, Shucheng 13; Zhang, Qi 14; Liu, Shangqing 15; Wang, Yunpeng 16; Li, Yuhui 17; He, Jian 18; Yang, Xiaoping 19
	2022-10-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	44 期号:10 页码:6695-6714
摘要	With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.
关键词	Benchmark testing Liver Image segmentation Biological systems Pancreas Computed tomography Kidney Multi-organ segmentation generalization semi-supervised learning weakly supervised learning continual learning
DOI	10.1109/TPAMI.2021.3100536
收录类别	SCI
语种	英语
资助项目	China's Ministry of Science and Technology[2020YFA0713800] ; National Natural Science Foundation of China[11971229] ; National Natural Science Foundation of China[12090023]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000853875300063
出版者	IEEE COMPUTER SOC
引用统计	被引频次：164[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19413
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yang, Xiaoping
作者单位	1.Nanjing Univ Sci & Technol, Dept Math, Nanjing 210094, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China 5.Shenzhen Haichuang Med CO LTD, Shenzhen 518000, Peoples R China 6.Jiangsu Univ Technol, Inst Bioinformat & Med Engn, Changzhou 213001, Peoples R China 7.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China 8.Beijing Infervis Technol CO LTD, Beijing 100089, Peoples R China 9.Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300222, Peoples R China 10.Norwegian Univ Sci & Technol, Dept Comp Sci, N-7491 Trondheim, Norway 11.Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China 12.Suzhou LungCare Med Technol Co Ltd, Suzhou 215021, Peoples R China 13.King Abdullah Univ Sci & Technol, Bioengn, Biol & Environm Sci & Engn Div, Thuwal 23955, Saudi Arabia 14.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Taipa 999078, Macau, Peoples R China 15.Southern Med Univ, Sch Biomed Engn, Guangzhou 510515, Peoples R China 16.Fudan Univ, Inst Biomed Sci, Shanghai 200433, Peoples R China 17.Univ Southern Calif, Computat Biol, Los Angeles, CA 90007 USA 18.Nanjing Drum Tower Hosp, Dept Nucl Med, Nanjing 210008, Peoples R China 19.Nanjing Univ, Dept Math, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Ma, Jun,Zhang, Yao,Gu, Song,et al. AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(10):6695-6714.
APA	Ma, Jun.,Zhang, Yao.,Gu, Song.,Zhu, Cheng.,Ge, Cheng.,...&Yang, Xiaoping.(2022).AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(10),6695-6714.
MLA	Ma, Jun,et al."AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.10(2022):6695-6714.