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

Tufvesson J, Hedström E, Steding-Ehrenborg K, Carlsson M, Arheden H, Heiberg E - Biomed Res Int (2015)

Bottom Line: Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50).Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden.

ABSTRACT

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 n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). 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.

No MeSH data available.


Related in: MedlinePlus

Initialization of segmentation (Step 3). The initializations of endocardial (red) and epicardial (green) borders resulting from Step 3 in the algorithm, shown in end-diastole (a) and end-systole (b) in the midventricular slice also used for Figure 1. The endocardial initialization is an estimation of the midmural line and the epicardial initialization is an estimation of the epicardial border.
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fig2: Initialization of segmentation (Step 3). The initializations of endocardial (red) and epicardial (green) borders resulting from Step 3 in the algorithm, shown in end-diastole (a) and end-systole (b) in the midventricular slice also used for Figure 1. The endocardial initialization is an estimation of the midmural line and the epicardial initialization is an estimation of the epicardial border.

Mentions: (b) Expanding the endocardial border estimated in Step 3 by one full wall thickness to get the epicardial initialization.


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

Tufvesson J, Hedström E, Steding-Ehrenborg K, Carlsson M, Arheden H, Heiberg E - Biomed Res Int (2015)

Initialization of segmentation (Step 3). The initializations of endocardial (red) and epicardial (green) borders resulting from Step 3 in the algorithm, shown in end-diastole (a) and end-systole (b) in the midventricular slice also used for Figure 1. The endocardial initialization is an estimation of the midmural line and the epicardial initialization is an estimation of the epicardial border.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4491381&req=5

fig2: Initialization of segmentation (Step 3). The initializations of endocardial (red) and epicardial (green) borders resulting from Step 3 in the algorithm, shown in end-diastole (a) and end-systole (b) in the midventricular slice also used for Figure 1. The endocardial initialization is an estimation of the midmural line and the epicardial initialization is an estimation of the epicardial border.
Mentions: (b) Expanding the endocardial border estimated in Step 3 by one full wall thickness to get the epicardial initialization.

Bottom Line: Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50).Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden.

ABSTRACT

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 n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). 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.

No MeSH data available.


Related in: MedlinePlus