Keywords: Analysis/Processing, Segmentation, Quantitative analysis
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.
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