Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2015, Article ID 970357, 12 pages
Research Article

Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging

1Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden
2Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden
3Department of Diagnostic Radiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden
4Department of Biomedical Engineering, Faculty of Engineering, Lund University, 221 00 Lund, Sweden

Received 1 August 2014; Accepted 12 January 2015

Academic Editor: Peter M. A. Van Ooijen

Copyright © 2015 Jane Tufvesson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set , test set ). Manual delineation was reference standard and second observer analysis was performed in a subset (). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.