Details

Autor(en) / Beteiligte

• Petitjean, Caroline
• Zuluaga, Maria A.
• Bai, Wenjia
• Dacher, Jean-Nicolas
• Grosgeorge, Damien
• Caudron, Jérôme
• Ruan, Su
• Ayed, Ismail Ben
• Cardoso, M. Jorge
• Chen, Hsiang-Chou
• Jimenez-Carretero, Daniel
• Ledesma-Carbayo, Maria J.
• Davatzikos, Christos
• Doshi, Jimit
• Erus, Guray
• Maier, Oskar M.O.
• Nambakhsh, Cyrus M.S.
• Ou, Yangming
• Ourselin, Sébastien
• Peng, Chun-Wei
• Peters, Nicholas S.
• Peters, Terry M.
• Rajchl, Martin
• Rueckert, Daniel
• Santos, Andres
• Shi, Wenzhe
• Wang, Ching-Wei
• Wang, Haiyan
• Yuan, Jing

Titel

Right ventricle segmentation from cardiac MRI: A collation study

Ist Teil von

• Medical image analysis, 2015-01, Vol.19 (1), p.187-202

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2015

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• [Display omitted] •Evaluation framework for right ventricle segmentation in short-axis MR images.•The datasets available for training and testing are described, as well as the manual reference.•Evaluation measures include technical and clinical indices.•Results obtained by 7 methods are presented.•The framework is open for new submissions. Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI’12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).