Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression

Background Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. Purpose To develop and validate a fully automated neural networ...

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Veröffentlicht in:Journal of magnetic resonance imaging Jg. 48; H. 1; S. 140 - 152
Hauptverfasser: Tan, Li Kuo, McLaughlin, Robert A., Lim, Einly, Abdul Aziz, Yang Faridah, Liew, Yih Miin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.07.2018
Schlagworte:
Abnormalities
Algorithms
automated segmentation
Automation
Blood volume
cardiac MRI
cine MRI
Coronary artery
deep learning
Diagnostic systems
Field strength
Heart
Image processing
Image segmentation
LV segmentation
Magnetic resonance imaging
Magnetohydrodynamics
Medical imaging
Neural networks
Protocol (computers)
Reproducibility
Standard data
Statistical analysis
Statistical significance
Statistical tests
Ventricle
deep learning
LV segmentation
cardiac MRI
automated segmentation
cine MRI
ISSN:1053-1807, 1522-2586, 1522-2586
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Zusammenfassung:Background Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. Purpose To develop and validate a fully automated neural network regression‐based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans. Study Type Cross‐sectional survey; diagnostic accuracy. Subjects In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database. Field Strength/Sequence 1.5T, steady‐state free precession. Assessment Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. Statistical Tests Paired t‐tests compared to previous work. Results Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and –19.8 ± 18.8 mL for the end‐systolic (ES) and end‐diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. Data Conclusion A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.25932