Right ventricle segmentation from cardiac MRI: A collation study

Petitjean C, Zuluaga MA, Bai W, Dacher JN, Grosgeorge D, Caudron J, Ruan S, Ben Ayed I, Cardoso MJ, Chen HC, Jimenez-Carretero D, Ledesma-Carbayo MJ, Davatzikos C, Doshi J, Erus G, Maier OMO, Nambakhsh CMS, Ou Y, Ourselin S, Peng CW, Peters NS, Peters TM, Rajchi M, Rueckert D, Santos A, Shi W, Wang CW, Wang H, Yuan J (2015)

Publication Type: Journal article

Publication year: 2015

Journal

Medical Image Analysis Elsevier

Book Volume: 19

Pages Range: 187-202

Journal Issue: 1

DOI: 10.1016/j.media.2014.10.004

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/).

Involved external institutions

University of Rouen / Université de Rouen FR France (FR) University College London (UCL) GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) GE Healthcare Sweden SE Sweden (SE) National Taiwan University of Science and Technology (NTUST) / 國立臺灣科技大學 / Taiwan Tech / 臺科大 TW Taiwan (TW) Universidad Politécnica de Madrid (UPM) ES Spain (ES) University of Pennsylvania US United States (USA) (US) Western University CA Canada (CA)

How to cite

APA:

Petitjean, C., Zuluaga, M.A., Bai, W., Dacher, J.-N., Grosgeorge, D., Caudron, J.,... Yuan, J. (2015). Right ventricle segmentation from cardiac MRI: A collation study. Medical Image Analysis, 19(1), 187-202. https://doi.org/10.1016/j.media.2014.10.004

MLA:

Petitjean, Caroline, et al. "Right ventricle segmentation from cardiac MRI: A collation study." Medical Image Analysis 19.1 (2015): 187-202.

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