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The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

文献类型：期刊论文

作者Heller, Nicholas8; Isensee, Fabian9,10; Maier-Hein, Klaus H.9; Hou, Xiaoshuai11; Xie, Chunmei11; Li, Fengyi11; Nan, Yang11; Mu, Guangrui12,13; Lin, Zhiyong14; Han, Miofei12
刊名MEDICAL IMAGE ANALYSIS
出版日期2021
卷号67页码:16
关键词Semantic segmentation Computed tomography Kidney tumor
ISSN号1361-8415
DOI10.1016/j.media.2020.101821
英文摘要There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average Sorensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation. (C) 2020 Elsevier B.V. All rights reserved.
资助项目National Cancer Institute of the National Institutes of Health[R01CA225435]
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000598891600009
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/16609]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Heller, Nicholas
作者单位1.Southeast Univ, Nanjing, Peoples R China
2.Lenovo Res, AI Lab, Beijing, Peoples R China
3.Nanjing Univ Sci & Technol, Sch Sci, Nanjing, Peoples R China
4.Univ Melbourne, Melbourne, Vic, Australia
5.Carleton Coll, Northfield, MN 55057 USA
6.Brigham Young Univ, Provo, UT 84602 USA
7.Univ North Dakota, Grand Forks, ND 58201 USA
8.Univ Minnesota, Minneapolis, MN 55455 USA
9.German Canc Res Ctr, Heidelberg, Germany
10.Heidelberg Univ, Heidelberg, Germany
推荐引用方式
GB/T 7714		 Heller, Nicholas,Isensee, Fabian,Maier-Hein, Klaus H.,et al. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge[J]. MEDICAL IMAGE ANALYSIS,2021,67:16.
APA Heller, Nicholas.,Isensee, Fabian.,Maier-Hein, Klaus H..,Hou, Xiaoshuai.,Xie, Chunmei.,...&Weight, Christopher.(2021).The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.MEDICAL IMAGE ANALYSIS,67,16.
MLA Heller, Nicholas,et al."The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge".MEDICAL IMAGE ANALYSIS 67(2021):16.

入库方式： OAI收割

来源：计算技术研究所

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