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.

organ segmentation

landmark detection

Evaluation framework

Författare

Oscar Jimenez-Del-Toro

Haute Ecole Specialisee de Suisse occidentale

Hopitaux universitaires de Geneve

Henning Muller

Haute Ecole Specialisee de Suisse occidentale

Hopitaux universitaires de Geneve

Markus Krenn

Medizinische Universitat Wien

Katharina Gruenberg

Universitatsklinikum Heidelberg

Abdel Aziz Taha

Technische Universität Wien

Marianne Winterstein

Universitatsklinikum 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 Universitat Wien

Georgios Kontokotsios

Technische Universität Wien

Georg Langs

Medizinische Universitat 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

Universitatsklinikum Heidelberg

Marc Andre Weber

Universitatsklinikum Heidelberg

Yashin Dicente Cid

Hopitaux universitaires de Geneve

Haute Ecole Specialisee de Suisse occidentale

Tobias Gass

Eidgenössische Technische Hochschule Zürich (ETH)

Mattias Heinrich

Universitat zu Lubeck

Fucang Jia

Chinese Academy of Sciences

Fredrik Kahl

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Razmig Kechichian

Université de Lyon

Dominic Mai

Universitat Freiburg im Breisgau

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)

Vol. 35 2459-2475 7488206

Ämneskategorier

Signalbehandling

DOI

10.1109/TMI.2016.2578680