Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks
| Autor: | Oscar Jimenez-del-Toro, Dominic Mai, Anna Walleyo, Abdel Aziz Taha, Antonio Foncubierta-Rodríguez, Daniel Wyeth, Georg Langs, Mattias P. Heinrich, Chunliang Wang, Henning Müller, Yashin Dicente Cid, Fredrik Kahl, Razmig Kéchichian, Ivan Eggel, Roger Schaer, Orcun Goksel, Markus Krenn, Tomas Salas Fernandez, Marianne Winterstein, Bjoern H. Menze, Georgios Kontokotsios, Katharina Gruenberg, Fucang Jia, Marc-André Weber, Andras Jakab, Assaf B. Spanier, Tobias Gass, G.R. Vincent, Allan Hanbury |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Male
Computer science landmark detection Evaluation framework organ segmentation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Computed tomography 030218 nuclear medicine & medical imaging Set (abstract data type) 03 medical and health sciences 0302 clinical medicine medicine Medical imaging Image Processing Computer-Assisted Humans Segmentation Electrical and Electronic Engineering Aged Landmark Radiological and Ultrasound Technology medicine.diagnostic_test Medicinsk bildbehandling Magnetic resonance imaging Image segmentation Anatomy Middle Aged Magnetic Resonance Imaging Computer Science Applications Data set Medical Image Processing Tomography x ray computed Female Anatomic Landmarks Tomography X-Ray Computed Algorithm 030217 neurology & neurosurgery Software Algorithms |
| Popis: | 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. QC 20170104 |
| Databáze: | OpenAIRE |
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