The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging : results of the KiTS19 challenge
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 d...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Heller, Nicholas [verfasserIn] Isensee, Fabian - 1990- [verfasserIn] Maier-Hein, Klaus H. - 1980- [verfasserIn] Hou, Xiaoshuai [verfasserIn] Xie, Chunmei [verfasserIn] Li, Fengyi [verfasserIn] Nan, Yang [verfasserIn] Mu, Guangrui [verfasserIn] Lin, Zhiyong [verfasserIn] Han, Miofei [verfasserIn] Yao, Guang [verfasserIn] Gao, Yaozong [verfasserIn] Zhang, Yao [verfasserIn] Wang, Yixin [verfasserIn] Hou, Feng [verfasserIn] Yang, Jiawei [verfasserIn] Xiong, Guangwei [verfasserIn] Tian, Jiang [verfasserIn] Zhong, Cheng [verfasserIn] Ma, Jun [verfasserIn] Rickman, Jack [verfasserIn] Dean, Joshua [verfasserIn] Stai, Bethany [verfasserIn] Tejpaul, Resha [verfasserIn] Oestreich, Makinna [verfasserIn] Blake, Paul [verfasserIn] Kaluzniak, Heather [verfasserIn] Raza, Shaneabbas [verfasserIn] Rosenberg, Joel [verfasserIn] Moore, Keenan [verfasserIn] Walczak, Edward [verfasserIn] Rengel, Zachary [verfasserIn] Edgerton, Zach [verfasserIn] Vasdev, Ranveer [verfasserIn] Peterson, Matthew [verfasserIn] McSweeney, Sean [verfasserIn] Peterson, Sarah [verfasserIn] Kalapara, Arveen [verfasserIn] Sathianathen, Niranjan [verfasserIn] Papanikolopoulos, Nikolaos [verfasserIn] Weight, Christopher [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
Available online 2 October 2020 Gesehen am 04.02.2022 |
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| Umfang: |
16 |
| Übergeordnetes Werk: |
Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier Science, 1996, 67(2021), Artikel-ID 101821, Seite 1-16 |
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| Übergeordnetes Werk: |
volume:67 ; year:2021 ; elocationid:101821 ; pages:1-16 ; extent:16 |
| Links: |
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| DOI / URN: |
10.1016/j.media.2020.101821 |
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1788499190 |
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| 100 | 1 | |a Heller, Nicholas |e verfasserin |0 (DE-588)1251019870 |0 (DE-627)1788522869 |4 aut | |
| 245 | 1 | 4 | |a The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging |b results of the KiTS19 challenge |c Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight |
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| 500 | |a Gesehen am 04.02.2022 | ||
| 520 | |a 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. | ||
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Kidney tumor | |
| 650 | 4 | |a Semantic segmentation | |
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| 700 | 1 | |a Tejpaul, Resha |e verfasserin |4 aut | |
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| 700 | 1 | |a Sathianathen, Niranjan |e verfasserin |4 aut | |
| 700 | 1 | |a Papanikolopoulos, Nikolaos |e verfasserin |4 aut | |
| 700 | 1 | |a Weight, Christopher |e verfasserin |4 aut | |
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10.1016/j.media.2020.101821 doi (DE-627)1788499190 (DE-599)KXP1788499190 (OCoLC)1341439576 DE-627 ger DE-627 rda eng Heller, Nicholas verfasserin (DE-588)1251019870 (DE-627)1788522869 aut The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging results of the KiTS19 challenge Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight 2021 16 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available online 2 October 2020 Gesehen am 04.02.2022 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. Computed tomography Kidney tumor Semantic segmentation Isensee, Fabian 1990- verfasserin (DE-588)1207568430 (DE-627)1694044998 aut Maier-Hein, Klaus H. 1980- verfasserin (DE-588)1100551875 (DE-627)85946461X (DE-576)333771222 aut Hou, Xiaoshuai verfasserin aut Xie, Chunmei verfasserin aut Li, Fengyi verfasserin aut Nan, Yang verfasserin aut Mu, Guangrui verfasserin aut Lin, Zhiyong verfasserin aut Han, Miofei verfasserin aut Yao, Guang verfasserin aut Gao, Yaozong verfasserin aut Zhang, Yao verfasserin aut Wang, Yixin verfasserin aut Hou, Feng verfasserin aut Yang, Jiawei verfasserin aut Xiong, Guangwei verfasserin aut Tian, Jiang verfasserin aut Zhong, Cheng verfasserin aut Ma, Jun verfasserin aut Rickman, Jack verfasserin aut Dean, Joshua verfasserin aut Stai, Bethany verfasserin aut Tejpaul, Resha verfasserin aut Oestreich, Makinna verfasserin aut Blake, Paul verfasserin aut Kaluzniak, Heather verfasserin aut Raza, Shaneabbas verfasserin aut Rosenberg, Joel verfasserin aut Moore, Keenan verfasserin aut Walczak, Edward verfasserin aut Rengel, Zachary verfasserin aut Edgerton, Zach verfasserin aut Vasdev, Ranveer verfasserin aut Peterson, Matthew verfasserin aut McSweeney, Sean verfasserin aut Peterson, Sarah verfasserin aut Kalapara, Arveen verfasserin aut Sathianathen, Niranjan verfasserin aut Papanikolopoulos, Nikolaos verfasserin aut Weight, Christopher verfasserin aut Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier Science, 1996 67(2021), Artikel-ID 101821, Seite 1-16 Online-Ressource (DE-627)306365081 (DE-600)1497450-2 (DE-576)091204941 1361-8423 nnns volume:67 year:2021 elocationid:101821 pages:1-16 extent:16 https://doi.org/10.1016/j.media.2020.101821 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S1361841520301857 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 67 2021 101821 1-16 16 2013 01 DE-16-250 4051245020 00 --%%-- --%%-- --%%-- --%%-- l01 04-02-22 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_41 2013 01 DE-16-250 03 s s_16 2013 01 DE-16-250 04 p (DE-627)1694045234 Isensee, Fabian 2013 01 DE-16-250 04 k (DE-627)1416535500 Fakultät für Biowissenschaften 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1551563029 Maier-Hein, Klaus H. 2013 01 DE-16-250 05 k (DE-627)1416741399 Radiologische Universitätsklinik 2013 01 DE-16-250 05 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 |
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10.1016/j.media.2020.101821 doi (DE-627)1788499190 (DE-599)KXP1788499190 (OCoLC)1341439576 DE-627 ger DE-627 rda eng Heller, Nicholas verfasserin (DE-588)1251019870 (DE-627)1788522869 aut The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging results of the KiTS19 challenge Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight 2021 16 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available online 2 October 2020 Gesehen am 04.02.2022 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. Computed tomography Kidney tumor Semantic segmentation Isensee, Fabian 1990- verfasserin (DE-588)1207568430 (DE-627)1694044998 aut Maier-Hein, Klaus H. 1980- verfasserin (DE-588)1100551875 (DE-627)85946461X (DE-576)333771222 aut Hou, Xiaoshuai verfasserin aut Xie, Chunmei verfasserin aut Li, Fengyi verfasserin aut Nan, Yang verfasserin aut Mu, Guangrui verfasserin aut Lin, Zhiyong verfasserin aut Han, Miofei verfasserin aut Yao, Guang verfasserin aut Gao, Yaozong verfasserin aut Zhang, Yao verfasserin aut Wang, Yixin verfasserin aut Hou, Feng verfasserin aut Yang, Jiawei verfasserin aut Xiong, Guangwei verfasserin aut Tian, Jiang verfasserin aut Zhong, Cheng verfasserin aut Ma, Jun verfasserin aut Rickman, Jack verfasserin aut Dean, Joshua verfasserin aut Stai, Bethany verfasserin aut Tejpaul, Resha verfasserin aut Oestreich, Makinna verfasserin aut Blake, Paul verfasserin aut Kaluzniak, Heather verfasserin aut Raza, Shaneabbas verfasserin aut Rosenberg, Joel verfasserin aut Moore, Keenan verfasserin aut Walczak, Edward verfasserin aut Rengel, Zachary verfasserin aut Edgerton, Zach verfasserin aut Vasdev, Ranveer verfasserin aut Peterson, Matthew verfasserin aut McSweeney, Sean verfasserin aut Peterson, Sarah verfasserin aut Kalapara, Arveen verfasserin aut Sathianathen, Niranjan verfasserin aut Papanikolopoulos, Nikolaos verfasserin aut Weight, Christopher verfasserin aut Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier Science, 1996 67(2021), Artikel-ID 101821, Seite 1-16 Online-Ressource (DE-627)306365081 (DE-600)1497450-2 (DE-576)091204941 1361-8423 nnns volume:67 year:2021 elocationid:101821 pages:1-16 extent:16 https://doi.org/10.1016/j.media.2020.101821 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S1361841520301857 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 67 2021 101821 1-16 16 2013 01 DE-16-250 4051245020 00 --%%-- --%%-- --%%-- --%%-- l01 04-02-22 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_41 2013 01 DE-16-250 03 s s_16 2013 01 DE-16-250 04 p (DE-627)1694045234 Isensee, Fabian 2013 01 DE-16-250 04 k (DE-627)1416535500 Fakultät für Biowissenschaften 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1551563029 Maier-Hein, Klaus H. 2013 01 DE-16-250 05 k (DE-627)1416741399 Radiologische Universitätsklinik 2013 01 DE-16-250 05 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 |
| allfields_unstemmed |
10.1016/j.media.2020.101821 doi (DE-627)1788499190 (DE-599)KXP1788499190 (OCoLC)1341439576 DE-627 ger DE-627 rda eng Heller, Nicholas verfasserin (DE-588)1251019870 (DE-627)1788522869 aut The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging results of the KiTS19 challenge Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight 2021 16 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available online 2 October 2020 Gesehen am 04.02.2022 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. Computed tomography Kidney tumor Semantic segmentation Isensee, Fabian 1990- verfasserin (DE-588)1207568430 (DE-627)1694044998 aut Maier-Hein, Klaus H. 1980- verfasserin (DE-588)1100551875 (DE-627)85946461X (DE-576)333771222 aut Hou, Xiaoshuai verfasserin aut Xie, Chunmei verfasserin aut Li, Fengyi verfasserin aut Nan, Yang verfasserin aut Mu, Guangrui verfasserin aut Lin, Zhiyong verfasserin aut Han, Miofei verfasserin aut Yao, Guang verfasserin aut Gao, Yaozong verfasserin aut Zhang, Yao verfasserin aut Wang, Yixin verfasserin aut Hou, Feng verfasserin aut Yang, Jiawei verfasserin aut Xiong, Guangwei verfasserin aut Tian, Jiang verfasserin aut Zhong, Cheng verfasserin aut Ma, Jun verfasserin aut Rickman, Jack verfasserin aut Dean, Joshua verfasserin aut Stai, Bethany verfasserin aut Tejpaul, Resha verfasserin aut Oestreich, Makinna verfasserin aut Blake, Paul verfasserin aut Kaluzniak, Heather verfasserin aut Raza, Shaneabbas verfasserin aut Rosenberg, Joel verfasserin aut Moore, Keenan verfasserin aut Walczak, Edward verfasserin aut Rengel, Zachary verfasserin aut Edgerton, Zach verfasserin aut Vasdev, Ranveer verfasserin aut Peterson, Matthew verfasserin aut McSweeney, Sean verfasserin aut Peterson, Sarah verfasserin aut Kalapara, Arveen verfasserin aut Sathianathen, Niranjan verfasserin aut Papanikolopoulos, Nikolaos verfasserin aut Weight, Christopher verfasserin aut Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier Science, 1996 67(2021), Artikel-ID 101821, Seite 1-16 Online-Ressource (DE-627)306365081 (DE-600)1497450-2 (DE-576)091204941 1361-8423 nnns volume:67 year:2021 elocationid:101821 pages:1-16 extent:16 https://doi.org/10.1016/j.media.2020.101821 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S1361841520301857 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 67 2021 101821 1-16 16 2013 01 DE-16-250 4051245020 00 --%%-- --%%-- --%%-- --%%-- l01 04-02-22 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_41 2013 01 DE-16-250 03 s s_16 2013 01 DE-16-250 04 p (DE-627)1694045234 Isensee, Fabian 2013 01 DE-16-250 04 k (DE-627)1416535500 Fakultät für Biowissenschaften 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1551563029 Maier-Hein, Klaus H. 2013 01 DE-16-250 05 k (DE-627)1416741399 Radiologische Universitätsklinik 2013 01 DE-16-250 05 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 |
| allfieldsGer |
10.1016/j.media.2020.101821 doi (DE-627)1788499190 (DE-599)KXP1788499190 (OCoLC)1341439576 DE-627 ger DE-627 rda eng Heller, Nicholas verfasserin (DE-588)1251019870 (DE-627)1788522869 aut The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging results of the KiTS19 challenge Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight 2021 16 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available online 2 October 2020 Gesehen am 04.02.2022 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. 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10.1016/j.media.2020.101821 doi (DE-627)1788499190 (DE-599)KXP1788499190 (OCoLC)1341439576 DE-627 ger DE-627 rda eng Heller, Nicholas verfasserin (DE-588)1251019870 (DE-627)1788522869 aut The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging results of the KiTS19 challenge Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight 2021 16 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Available online 2 October 2020 Gesehen am 04.02.2022 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. Computed tomography Kidney tumor Semantic segmentation Isensee, Fabian 1990- verfasserin (DE-588)1207568430 (DE-627)1694044998 aut Maier-Hein, Klaus H. 1980- verfasserin (DE-588)1100551875 (DE-627)85946461X (DE-576)333771222 aut Hou, Xiaoshuai verfasserin aut Xie, Chunmei verfasserin aut Li, Fengyi verfasserin aut Nan, Yang verfasserin aut Mu, Guangrui verfasserin aut Lin, Zhiyong verfasserin aut Han, Miofei verfasserin aut Yao, Guang verfasserin aut Gao, Yaozong verfasserin aut Zhang, Yao verfasserin aut Wang, Yixin verfasserin aut Hou, Feng verfasserin aut Yang, Jiawei verfasserin aut Xiong, Guangwei verfasserin aut Tian, Jiang verfasserin aut Zhong, Cheng verfasserin aut Ma, Jun verfasserin aut Rickman, Jack verfasserin aut Dean, Joshua verfasserin aut Stai, Bethany verfasserin aut Tejpaul, Resha verfasserin aut Oestreich, Makinna verfasserin aut Blake, Paul verfasserin aut Kaluzniak, Heather verfasserin aut Raza, Shaneabbas verfasserin aut Rosenberg, Joel verfasserin aut Moore, Keenan verfasserin aut Walczak, Edward verfasserin aut Rengel, Zachary verfasserin aut Edgerton, Zach verfasserin aut Vasdev, Ranveer verfasserin aut Peterson, Matthew verfasserin aut McSweeney, Sean verfasserin aut Peterson, Sarah verfasserin aut Kalapara, Arveen verfasserin aut Sathianathen, Niranjan verfasserin aut Papanikolopoulos, Nikolaos verfasserin aut Weight, Christopher verfasserin aut Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier Science, 1996 67(2021), Artikel-ID 101821, Seite 1-16 Online-Ressource (DE-627)306365081 (DE-600)1497450-2 (DE-576)091204941 1361-8423 nnns volume:67 year:2021 elocationid:101821 pages:1-16 extent:16 https://doi.org/10.1016/j.media.2020.101821 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S1361841520301857 Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 67 2021 101821 1-16 16 2013 01 DE-16-250 4051245020 00 --%%-- --%%-- --%%-- --%%-- l01 04-02-22 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_41 2013 01 DE-16-250 03 s s_16 2013 01 DE-16-250 04 p (DE-627)1694045234 Isensee, Fabian 2013 01 DE-16-250 04 k (DE-627)1416535500 Fakultät für Biowissenschaften 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1551563029 Maier-Hein, Klaus H. 2013 01 DE-16-250 05 k (DE-627)1416741399 Radiologische Universitätsklinik 2013 01 DE-16-250 05 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3 |
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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 |
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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 Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight |
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Heller, Nicholas Isensee, Fabian Maier-Hein, Klaus H. Hou, Xiaoshuai Xie, Chunmei Li, Fengyi Nan, Yang Mu, Guangrui Lin, Zhiyong Han, Miofei Yao, Guang Gao, Yaozong Zhang, Yao Wang, Yixin Hou, Feng Yang, Jiawei Xiong, Guangwei Tian, Jiang Zhong, Cheng Ma, Jun Rickman, Jack Dean, Joshua Stai, Bethany Tejpaul, Resha Oestreich, Makinna Blake, Paul Kaluzniak, Heather Raza, Shaneabbas Rosenberg, Joel Moore, Keenan Walczak, Edward Rengel, Zachary Edgerton, Zach Vasdev, Ranveer Peterson, Matthew McSweeney, Sean Peterson, Sarah Kalapara, Arveen Sathianathen, Niranjan Papanikolopoulos, Nikolaos Weight, Christopher |
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state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imagingresults of the kits19 challenge |
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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 |
| abstract |
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. Available online 2 October 2020 Gesehen am 04.02.2022 |
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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. Available online 2 October 2020 Gesehen am 04.02.2022 |
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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. Available online 2 October 2020 Gesehen am 04.02.2022 |
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