Right ventricle segmentation from cardiac MRI: A collation study
Right ventricle segmentation from cardiac MRI: A collation study
Petitjean, Caroline and Zuluaga, Maria A. and Bai, Wenjia and Dacher, Jean Nicolas and Grosgeorge, Damien and Caudron, JérÔme and Ruan, Su and Ayed, Ismail Ben and Cardoso, M. Jorge and Chen, Hsiang Chou and Jimenez-Carretero, Daniel and Ledesma-Carbayo, Maria J. and Davatzikos, Christos and Doshi, Jimit and Erus, Guray and Maier, Oskar M.O. and Nambakhsh, Cyrus M.S. and Ou, Yangming and Ourselin, Sébastien and Peng, Chun Wei and Peters, Nicholas S. and Peters, Terry M. and Rajchl, Martin and Rueckert, Daniel and Santos, Andres and Shi, Wenzhe and Wang, Ching Wei and Wang, Haiyan and Yuan, Jing
Medical Image Analysis 2015
Abstract : 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 1. cm 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/).
