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Sökning: onr:"swepub:oai:DiVA.org:kth-198890" > Cloud-Based Evaluat...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006327naa a2200721 4500
001oai:DiVA.org:kth-198890
003SwePub
008161222s2016 | |||||||||||000 ||eng|
009oai:research.chalmers.se:776fa12c-8416-434d-85d2-826711527718
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1988902 URI
0247 a https://doi.org/10.1109/TMI.2016.25786802 DOI
0247 a https://research.chalmers.se/publication/2463752 URI
040 a (SwePub)kthd (SwePub)cth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Jimenez-del-Toro, Oscaru Hopitaux universitaires de Geneve4 aut
2451 0a Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms :b VISCERAL Anatomy Benchmarks
264 1b Institute of Electrical and Electronics Engineers (IEEE),c 2016
338 a print2 rdacarrier
500 a QC 20170104
520 a Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
650 7a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
650 7a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Signalbehandling0 (SwePub)202052 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Signal Processing0 (SwePub)202052 hsv//eng
653 a Evaluation framework
653 a organ segmentation
653 a landmark detection
7001 a Muller, Henningu Hopitaux universitaires de Geneve4 aut
7001 a Krenn, Markusu Medizinische Universität Wien,Medical University of Vienna4 aut
7001 a Gruenberg, Katharinau Universitätsklinikum Heidelberg,University Hospital Heidelberg4 aut
7001 a Taha, Abdel Azizu Technische Universität Wien,Vienna University of Technology4 aut
7001 a Winterstein, Marianneu Universitätsklinikum Heidelberg,University Hospital Heidelberg4 aut
7001 a Eggel, Ivan4 aut
7001 a Foncubierta-Rodriguez, Antoniou Eidgenössische Technische Hochschule Zürich (ETH),Swiss Federal Institute of Technology in Zürich (ETH)4 aut
7001 a Goksel, Orcunu Eidgenössische Technische Hochschule Zürich (ETH),Swiss Federal Institute of Technology in Zürich (ETH)4 aut
7001 a Jakab, Andresu Medizinische Universität Wien,Medical University of Vienna4 aut
7001 a Kontokotsios, Georgiosu Technische Universität Wien,Vienna University of Technology4 aut
7001 a Langs, Georgu Medizinische Universität Wien,Medical University of Vienna4 aut
7001 a Menze, Bjoern H.u Eidgenössische Technische Hochschule Zürich (ETH),Swiss Federal Institute of Technology in Zürich (ETH)4 aut
7001 a Fernandez, Tomas Salasu Eidgenössische Technische Hochschule Zürich (ETH),Swiss Federal Institute of Technology in Zürich (ETH),Agency for Health Quality and Assessment of Catalonia4 aut
7001 a Schaer, Roger4 aut
7001 a Walleyo, Annau Universitätsklinikum Heidelberg,University Hospital Heidelberg4 aut
7001 a Weber, Marc-Andreu Universitätsklinikum Heidelberg,University Hospital Heidelberg4 aut
7001 a Cid, Yashin Dicenteu Hopitaux universitaires de Geneve4 aut
7001 a Gass, Tobiasu Eidgenössische Technische Hochschule Zürich (ETH),Swiss Federal Institute of Technology in Zürich (ETH)4 aut
7001 a Heinrich, Mattiasu Universitaet Zu Lübeck4 aut
7001 a Jia, Fucangu Chinese Academy of Sciences4 aut
7001 a Kahl, Fredrik,d 1972u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)kahlf
7001 a Kechichian, Razmigu Université de Lyon4 aut
7001 a Mai, Dominicu Albert-Ludwigs-Universität Freiburg,University of Freiburg4 aut
7001 a Spanier, Assaf B.u The Hebrew University Of Jerusalem4 aut
7001 a Vincent, Graham4 aut
7001 a Wang, Chunliangu KTH,Medicinsk bildbehandling och visualisering,Kungliga Tekniska Högskolan (KTH),Royal Institute of Technology (KTH)4 aut0 (Swepub:kth)u1tbkeej
7001 a Wyeth, Danielu Toshiba Medical Visualization Systems Europe4 aut
7001 a Hanbury, Allanu Technische Universität Wien,Vienna University of Technology4 aut
7102 a Hopitaux universitaires de Geneveb Medizinische Universität Wien4 org
7730 t IEEE Transactions on Medical Imagingd : Institute of Electrical and Electronics Engineers (IEEE)g 35:11, s. 2459-2475q 35:11<2459-2475x 0278-0062x 1558-254X
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198890
8564 8u https://doi.org/10.1109/TMI.2016.2578680
8564 8u https://research.chalmers.se/publication/246375

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