Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks
Artikel i vetenskaplig tidskrift, 2016
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.
Evaluation framework
landmark detection
organ segmentation
Författare
Oscar Jimenez-Del-Toro
Hopitaux universitaires de Geneve
Haute Ecole Specialisee de Suisse occidentale
Henning Muller
Haute Ecole Specialisee de Suisse occidentale
Hopitaux universitaires de Geneve
Markus Krenn
Medizinische Universität Wien
Katharina Gruenberg
Universitätsklinikum Heidelberg
Abdel Aziz Taha
Technische Universität Wien
Marianne Winterstein
Universitätsklinikum Heidelberg
Ivan Eggel
Haute Ecole Specialisee de Suisse occidentale
Antonio Foncubierta-Rodriguez
Eidgenössische Technische Hochschule Zürich (ETH)
Orcun Goksel
Eidgenössische Technische Hochschule Zürich (ETH)
Andras Jakab
Medizinische Universität Wien
Georgios Kontokotsios
Technische Universität Wien
Georg Langs
Medizinische Universität Wien
Bjoern H. Menze
Eidgenössische Technische Hochschule Zürich (ETH)
Tomas Salas Fernandez
Eidgenössische Technische Hochschule Zürich (ETH)
Agency for Health Quality and Assessment of Catalonia
Roger Schaer
Haute Ecole Specialisee de Suisse occidentale
Anna Walleyo
Universitätsklinikum Heidelberg
Marc Andre Weber
Universitätsklinikum Heidelberg
Yashin Dicente Cid
Haute Ecole Specialisee de Suisse occidentale
Hopitaux universitaires de Geneve
Tobias Gass
Eidgenössische Technische Hochschule Zürich (ETH)
Mattias Heinrich
Universitaet Zu Lübeck
Fucang Jia
Chinese Academy of Sciences
Fredrik Kahl
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
Razmig Kechichian
Université de Lyon
Dominic Mai
Albert-Ludwigs-Universität Freiburg
Assaf B. Spanier
The Hebrew University Of Jerusalem
Graham Vincent
Imorphics Ltd
C. L. Wang
Kungliga Tekniska Högskolan (KTH)
Daniel Wyeth
Toshiba Medical Visualization Systems Europe
Allan Hanbury
Technische Universität Wien
IEEE Transactions on Medical Imaging
0278-0062 (ISSN) 1558254x (eISSN)
Vol. 35 11 2459-2475 7488206Ämneskategorier
Signalbehandling
DOI
10.1109/TMI.2016.2578680