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

BackgroundLeft 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. PurposeTo 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 TypeCross-sectional survey; diagnostic accuracy. SubjectsIn 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/Sequence1.5T, steady-state free precession. AssessmentReference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. Statistical TestsPaired t-tests compared to previous work. ResultsTested against the LVSC database, we obtained 0.770.11 (Jaccard index) and 1.330.71mm (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 +7.213.0mL and -19.818.8mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. Data ConclusionA 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.