TY - JOUR
T1 - The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging
T2 - Results of the KiTS19 challenge
AU - Heller, Nicholas
AU - Isensee, Fabian
AU - Maier-Hein, Klaus H.
AU - Hou, Xiaoshuai
AU - Xie, Chunmei
AU - Li, Fengyi
AU - Nan, Yang
AU - Mu, Guangrui
AU - Lin, Zhiyong
AU - Han, Miofei
AU - Yao, Guang
AU - Gao, Yaozong
AU - Zhang, Yao
AU - Wang, Yixin
AU - Hou, Feng
AU - Yang, Jiawei
AU - Xiong, Guangwei
AU - Tian, Jiang
AU - Zhong, Cheng
AU - Ma, Jun
AU - Rickman, Jack
AU - Dean, Joshua
AU - Stai, Bethany
AU - Tejpaul, Resha
AU - Oestreich, Makinna
AU - Blake, Paul
AU - Kaluzniak, Heather
AU - Raza, Shaneabbas
AU - Rosenberg, Joel
AU - Moore, Keenan
AU - Walczak, Edward
AU - Rengel, Zachary
AU - Edgerton, Zach
AU - Vasdev, Ranveer
AU - Peterson, Matthew
AU - McSweeney, Sean
AU - Peterson, Sarah
AU - Kalapara, Arveen
AU - Sathianathen, Niranjan
AU - Papanikolopoulos, Nikolaos
AU - Weight, Christopher
N1 - Publisher Copyright:
© 2020
PY - 2021/1
Y1 - 2021/1
N2 - 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 Sørensen-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.
AB - 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 Sørensen-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.
KW - Computed tomography
KW - Kidney tumor
KW - Semantic segmentation
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U2 - 10.1016/j.media.2020.101821
DO - 10.1016/j.media.2020.101821
M3 - Article
C2 - 33049579
AN - SCOPUS:85092340246
SN - 1361-8415
VL - 67
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 101821
ER -