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"Fully-Automatic Left Ventricular Segmentation from Long-Axis Cardiac Cine MR Scans"

Rahil Shahzad, Qian Tao, Oleh Dzyubachyk, Marius Staring, Boudewijn P.F. Lelieveldt and Rob J. van der Geest

Abstract

With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans. The pipeline is based on a multi-atlas-based segmentation approach and a spatio-temporal registration approach. The performance of the method was assessed by: (i) comparing the automatic segmentations to those obtained manually at both the end-diastolic and end-systolic phase, (ii) comparing the automatically obtained clinical parameters, including end-diastolic volume, end-systolic volume, stroke volume and ejection fraction, with those defined manually and (iii) by the accuracy of classifying subjects to the appropriate risk category based on the estimated ejection fraction. Automatic segmentation of the left ventricular endocardium was achieved with a Dice similarity coefficient (DSC) of 0.93 on the end-diastolic phase for both the vertical and horizontal long-axis scan; on the end-systolic phase the DSC was 0.88 and 0.85, respectively. For the epicardium, a DSC of 0.94 and 0.95 was obtained on the end-diastolic vertical and horizontal long-axis scans; on the end-systolic phase the DSC was 0.90 and 0.88, respectively. With respect to the clinical volumetric parameters, Pearson correlation coefficient (R) of 0.97 was obtained for the end-diastolic volume, 0.95 for end-systolic volume, 0.87 for stroke volume and 0.84 for ejection fraction. Risk category classification based on ejection fraction showed that 80% of the subjects were assigned to the correct risk category and only one subject (< 1%) was more than one risk category off. We conclude that the proposed automatic pipeline presents a viable and cost-effective alternative for manual annotation.

 

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Copyright © 2017 by the authors. Published version © 2017 by Elsevier Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Shahzad:2017,
author = "{Rahil Shahzad and Qian Tao and Oleh Dzyubachyk and Marius Staring and Boudewijn P.F. Lelieveldt and Rob J. van der Geest}",
title = "{Fully-Automatic Left Ventricular Segmentation from Long-Axis Cardiac Cine MR Scans}",
journal = "{Medical Image Analysis}",
volume = "{39}",
pages = "{44 - 55}",
month = "{July}",
year = "{2017}",
}

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