Deep learning for quantification of myocardial scar and microvascular obstruction in late gadolinium enhancement cardiovascular magnetic resonance images

Abstract Objectives Detecting and measuring myocardial scar and microvascular obstruction (MVO) accurately following reperfusion therapy is clinically vital for patients with acute myocardial infarction (AMI). Current clinical practice relies on manual delineation of endocardial borders as well as hyper- and hypo-enhanced regions on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images for scar and MVO quantification, respectively. However, this approach is subjective, labor-intensive, and time-consuming. The objective of this study is to develop and assess a convolutional neural network (CNN)-based method for the automated quantification of LGE scar and MVO in AMI patients. Methods A total of 1836 LGE images from 252 patients with AMI were analyzed, with the cohort comprising 232 males and an average age of 56±9 years. Our approach, utilizing a combined architecture of cascaded YOLOv8 (You Only Look Once version 8) and nnU-Net, was implemented for the simultaneous segmentation of the left ventricular (LV) myocardium, scar, and MVO in LGE images. Independent datasets for training, validation, and testing were maintained in an 8:1:1 ratio. Performance evaluation was conducted by comparing the results to manual scar and MVO quantification, utilizing metrics such as the Dice similarity coefficient (DSC) and Bland-Altman analysis. Results The automated segmentation process took 0.05 seconds per image. The segmentation accuracy, as assessed by the DSC, yielded values of 0.96±0.04 (per-patient, n=25) and 0.96±0.05 (per-slice, n=184) for scar segmentation, and 0.86±0.17 (per-patient) and 0.89±0.15 (per-slice) for MVO segmentation. In per-patient analysis, the volumes of automatically segmented scar and MVO demonstrated strong agreement with manually derived results, with biases [limits of agreement] of -0.3 [-3.0, 2.4] cm³ and 0.1 [-0.2, 0.3] cm³, respectively. Furthermore, there was strong concordance between automatically and manually derived scar size as a percentage of LV mass (-0.2 [-3.1, 2.7] %) and MVO size as a percentage of scar (1.2 [-5.6, 8.0] %). Conclusions The findings demonstrate excellent agreement between our proposed automated deep-learning solution and clinical expert annotation, suggesting its potential as a predictive tool for prognosis and clinical outcomes in patients with AMI.