Abstract #4794

Automatic Segmentation Of The Myocardium in Cardiac Arterial Spin Labelling Images Using a Deep Learning Model Facilitates Myocardial Blood Flow Quantification

Pedro M. Gordaliza^1,2, Verónica Aramendía‐Vidaurreta³, Juan José Vaquero^1,2, Gorka Bastarrika³, María Asunción Fernández-Seara³, and María Arrate Muñoz-Barrutia^1,2

¹Bioengineering, Universidad Carlos III de Madrid, Leganés, Spain, ²Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain, ³Radiology Deparment, Clínica Universidad de Navarra, Pamplona, Spain

Arterial Spin Labelling (ASL) allows to quantify Myocardial Blood Flow (MBF) by averaging over multiple ASL pairs. However, the procedure heavily depends on the manual segmentation of the myocardium. In this work, we introduce a Deep Learning model to segment this region and build a completely automatic pipeline for the MBF estimation. The accomplished evaluation results prove the success of the proposed method, which presents: 1) high overlap between the automatically extracted masks and those manually segmented by an expert (Dice Similarity Coefficient around 90%) and 2) good agreement of the MBF estimations with those obtained from the manual annotations.

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