Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks
Journal article, 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

Author

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 Universitaet Wien

Katharina Gruenberg

Universitatsklinikum Heidelberg

Abdel Aziz Taha

Vienna University of Technology

Marianne Winterstein

Universitatsklinikum Heidelberg

Ivan Eggel

Haute Ecole Specialisee de Suisse occidentale

Antonio Foncubierta-Rodriguez

Swiss Federal Institute of Technology in Zürich (ETH)

Orcun Goksel

Swiss Federal Institute of Technology in Zürich (ETH)

Andras Jakab

Medizinische Universitaet Wien

Georgios Kontokotsios

Vienna University of Technology

Georg Langs

Medizinische Universitaet Wien

Bjoern H. Menze

Swiss Federal Institute of Technology in Zürich (ETH)

Tomas Salas Fernandez

Swiss Federal Institute of Technology in 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

Haute Ecole Specialisee de Suisse occidentale

Hopitaux universitaires de Geneve

Tobias Gass

Swiss Federal Institute of Technology in Zürich (ETH)

Mattias Heinrich

Universitaet Zu Lübeck

Fucang Jia

Chinese Academy of Sciences

Fredrik Kahl

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Razmig Kechichian

Université de Lyon

Dominic Mai

University of Freiburg

Assaf B. Spanier

The Hebrew University Of Jerusalem

Graham Vincent

Imorphics Ltd

C. L. Wang

Royal Institute of Technology (KTH)

Daniel Wyeth

Toshiba Medical Visualization Systems Europe

Allan Hanbury

Vienna University of Technology

IEEE Transactions on Medical Imaging

0278-0062 (ISSN)

Vol. 35 11 2459-2475 7488206

Subject Categories

Signal Processing

DOI

10.1109/TMI.2016.2578680

More information

Latest update

9/6/2018 1