Deep Learning-Based Quasi-Automatic Tool for Regional Quantitative 17-Segment Analysis of Myocardial Fibrosis

Motivation: Despite the significance of regional myocardial analysis in clinical practice it's performed manually, which is a time-consuming task. Therefore, automation of myocardium regional analysis is a relevant task. Goal(s): The goal of this work is to develop a tool for myocardium regional quantitative analysis automation Approach: A trained neural network segment myocardium and fibrosis. The segmented myocardium undergoes additional segmentation into 17 segments using mathematical algorithm. The fibrosis volume in each segment is measured. Results: U-Net achieved median DSC 0.75 for fibrosis and 0.85 myocardium. The fibrosis regional detection accuracy of our algorithm 0.71 according to F-score. Our algorithm speed is about 30s/patient. Impact: Our tool allows to speed up and improve the accuracy of myocardium regional analysis.