Preliminary data on a fully automated left ventricular late gadolinium enhancement detection by a convolutional neuronal network in chronic myocardial infarction

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): FWF- Der Wissenschaftsfonds Aim To compare a fully automated segmentation of left ventricular late gadolinium enhancement (LGE) as evaluated by a convolutional neuronal network (CNN) with manual segmentation in chronic myocardial infarction. Methods Cardiac magnetic resonance imaging including two-dimensional LGE imaging was performed in 191 patients on a 1.5 T clinical scanner 12 months after ST-elevation myocardial infarction. LGE images were presented to a trained CNN for automated determination of left ventricular myocardium and consequently absolute LGE volume. Manual LGE segmentation according to the +5-SD method was used as reference standard. Image quality was assessed according to a 3-point Likert scale (2 = perfect image quality, 1 = some artifacts witout impaired LGE delineation, 0 = strong artifacts with impaired LGE delineation). Regression and Bland-Altman analysis were performed. Results In 191 included patients (182 male, mean age 57 years) LGE volume was 9.7 [IQR 3.6 to 16.2] ml according to manual segmentation and 8.3 [3.2 to 17.6] ml according to CNN segmentation. Bland-Altman analysis showed little average difference (-0.5 ml, p=0.257), however, limits of agreement ranged from -18.4 ml to 17.5 ml. Linear correlation was fair (0.57, p<0.001). Subgroup analysis according to image quality showed comparable performance of CNN segmentation in all three groups. Conclusion Our fully automated LGE segmentation based on a CNN in two-dimensional data sets provides measurements with little average difference compared to very time-consuming manual segmentations. However, dispersion is substantially and limits the current application of this approach on a per-patient basis. Image quality does not affect CNN performance.