Right ventricle segmentation from cardiac MRI: A collation study

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 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/).

A Deep Learning Network for Right Ventricle Segmentation in Short:Axis MRI

“…However, most of researches focus on the left ventricle (LV) segmentation because the LV is the biggest chamber of heart [3][4][5]. Compared to the LV, the segmentation problems of RV get more and more attentions in recent years, along with the increasing cognition about the function of RV [6][7][8]. For instance, some cardiac diseases such as pulmonary hypertension, cardiomyopathy and dysplasia, are all related to the RV.…”

Right ventricular segmentation in cardiac MRI with moving mesh correspondences

“…The proposed method was evaluated over a data set composed of 48 subjects provided by the RVSC, MICCAI 2012 [16] and a data set composed of 23 Hypoplastic Left Heart Syndrome (HLHS) patients. For the moving mesh, grid sampling was set equal to the pixel spacing.…”

“…The proposed method was evaluated over the training and testing sets provided by the RVSC [16]. Each data set consists of short-axis MRI volumes of 16 subjects.…”

“…Therefore, it is a common clinical practice to delineate the RV boundaries only in end diastolic and end systolic phases. Automating the process has bestirred a significant research attention recently, and has been the subject of a Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012 RV Segmentation Challenge (RVSC) [11][12][13][14][15][16][17][18][19].…”

Papillary Muscle Segmentation from a Multi-atlas Database: A Feasibility Study

“…There is a wide range of multi-atlas based approaches addressing the problem of cardiac segmentation [7]. We have selected to use the segmentation pipeline proposed by Zuluaga et al [10] as it has shown to be robust in the segmentation of different structures within the heart [13,14]. The details of the presented method can be found in [10].…”