001     180698
005     20240229145629.0
024 7 _ |a 10.1038/s41467-022-30695-9
|2 doi
024 7 _ |a pmid:35840566
|2 pmid
024 7 _ |a altmetric:132652279
|2 altmetric
037 _ _ |a DKFZ-2022-01494
041 _ _ |a English
082 _ _ |a 500
100 1 _ |a Antonelli, Michela
|0 0000-0002-3005-4523
|b 0
245 _ _ |a The Medical Segmentation Decathlon.
260 _ _ |a [London]
|c 2022
|b Nature Publishing Group UK
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1658138861_17496
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a #EA:E130#LA:E130#
520 _ _ |a International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.
536 _ _ |a 315 - Bildgebung und Radioonkologie (POF4-315)
|0 G:(DE-HGF)POF4-315
|c POF4-315
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
700 1 _ |a Reinke, Annika
|0 P:(DE-He78)97e904f47dab556a77c0149cd0002591
|b 1
|e First author
700 1 _ |a Bakas, Spyridon
|0 0000-0001-8734-6482
|b 2
700 1 _ |a Farahani, Keyvan
|b 3
700 1 _ |a Kopp-Schneider, Annette
|0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596
|b 4
700 1 _ |a Landman, Bennett A
|0 0000-0001-5733-2127
|b 5
700 1 _ |a Litjens, Geert
|0 0000-0003-1554-1291
|b 6
700 1 _ |a Menze, Bjoern
|0 0000-0003-4136-5690
|b 7
700 1 _ |a Ronneberger, Olaf
|b 8
700 1 _ |a Summers, Ronald M
|b 9
700 1 _ |a van Ginneken, Bram
|b 10
700 1 _ |a Bilello, Michel
|b 11
700 1 _ |a Bilic, Patrick
|b 12
700 1 _ |a Christ, Patrick F
|b 13
700 1 _ |a Do, Richard K G
|0 0000-0002-6554-0310
|b 14
700 1 _ |a Gollub, Marc J
|b 15
700 1 _ |a Heckers, Stephan H
|b 16
700 1 _ |a Huisman, Henkjan
|0 0000-0001-6753-3221
|b 17
700 1 _ |a Jarnagin, William R
|b 18
700 1 _ |a McHugo, Maureen K
|b 19
700 1 _ |a Napel, Sandy
|0 0000-0002-6876-5507
|b 20
700 1 _ |a Pernicka, Jennifer S Golia
|0 0000-0002-1076-7948
|b 21
700 1 _ |a Rhode, Kawal
|b 22
700 1 _ |a Tobon-Gomez, Catalina
|b 23
700 1 _ |a Vorontsov, Eugene
|b 24
700 1 _ |a Meakin, James A
|b 25
700 1 _ |a Ourselin, Sebastien
|b 26
700 1 _ |a Wiesenfarth, Manuel
|0 P:(DE-He78)1042737c83ba70ec508bdd99f0096864
|b 27
700 1 _ |a Arbeláez, Pablo
|b 28
700 1 _ |a Bae, Byeonguk
|0 0000-0003-2309-8517
|b 29
700 1 _ |a Chen, Sihong
|b 30
700 1 _ |a Daza, Laura
|b 31
700 1 _ |a Feng, Jianjiang
|0 0000-0002-5940-0063
|b 32
700 1 _ |a He, Baochun
|b 33
700 1 _ |a Isensee, Fabian
|0 P:(DE-He78)7ea9af59d03ec7deb982a0e0562358fa
|b 34
700 1 _ |a Ji, Yuanfeng
|b 35
700 1 _ |a Jia, Fucang
|0 0000-0003-0075-979X
|b 36
700 1 _ |a Kim, Ildoo
|b 37
700 1 _ |a Maier-Hein, Klaus
|b 38
700 1 _ |a Merhof, Dorit
|0 0000-0002-1672-2185
|b 39
700 1 _ |a Pai, Akshay
|b 40
700 1 _ |a Park, Beomhee
|b 41
700 1 _ |a Perslev, Mathias
|0 0000-0002-0358-4692
|b 42
700 1 _ |a Rezaiifar, Ramin
|b 43
700 1 _ |a Rippel, Oliver
|b 44
700 1 _ |a Sarasua, Ignacio
|b 45
700 1 _ |a Shen, Wei
|b 46
700 1 _ |a Son, Jaemin
|b 47
700 1 _ |a Wachinger, Christian
|b 48
700 1 _ |a Wang, Liansheng
|b 49
700 1 _ |a Wang, Yan
|b 50
700 1 _ |a Xia, Yingda
|b 51
700 1 _ |a Xu, Daguang
|b 52
700 1 _ |a Xu, Zhanwei
|0 0000-0003-0225-7662
|b 53
700 1 _ |a Zheng, Yefeng
|0 0000-0003-2195-2847
|b 54
700 1 _ |a Simpson, Amber L
|b 55
700 1 _ |a Maier-Hein, Lena
|0 P:(DE-He78)26a1176cd8450660333a012075050072
|b 56
|e Last author
700 1 _ |a Cardoso, M Jorge
|0 0000-0003-1284-2558
|b 57
773 _ _ |a 10.1038/s41467-022-30695-9
|g Vol. 13, no. 1, p. 4128
|0 PERI:(DE-600)2553671-0
|n 1
|p 4128
|t Nature Communications
|v 13
|y 2022
|x 2041-1723
909 C O |o oai:inrepo02.dkfz.de:180698
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-He78)97e904f47dab556a77c0149cd0002591
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 4
|6 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 27
|6 P:(DE-He78)1042737c83ba70ec508bdd99f0096864
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 34
|6 P:(DE-He78)7ea9af59d03ec7deb982a0e0562358fa
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 56
|6 P:(DE-He78)26a1176cd8450660333a012075050072
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-315
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Bildgebung und Radioonkologie
|x 0
914 1 _ |y 2022
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-02-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-02
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2021-02-02
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2021-02-02
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NAT COMMUN : 2021
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-10-13T14:44:21Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-10-13T14:44:21Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
|d 2021-10-13T14:44:21Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1040
|2 StatID
|b Zoological Record
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
|d 2022-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2022-11-11
915 _ _ |a IF >= 15
|0 StatID:(DE-HGF)9915
|2 StatID
|b NAT COMMUN : 2021
|d 2022-11-11
920 2 _ |0 I:(DE-He78)E130-20160331
|k E130
|l E130 Intelligente Medizinische Systeme
|x 0
920 1 _ |0 I:(DE-He78)E130-20160331
|k E130
|l E130 Intelligente Medizinische Systeme
|x 0
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 1
920 1 _ |0 I:(DE-He78)E230-20160331
|k E230
|l E230 Medizinische Bildverarbeitung
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)E130-20160331
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a I:(DE-He78)E230-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21