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Abstract #5126

Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry

Tina Yao1,2, Nicole St. Clair3, John Gold3, Gabriel Miller3, David Schidlow3, Sunil Ghelani3, Rahul Rathod3, Jennifer Steeden1, and Vivek Muthurangu1
1Institute of Cardiovascular Science, University College London, London, United Kingdom, 2Institute of Health Informatics, University College London, London, United Kingdom, 3Department of Cardiology, Boston Children's Hospital, Boston, MA, United States

Synopsis

Keywords: Heart, Machine Learning/Artificial IntelligenceWe have created an end-to-end machine learning pipeline that takes cardiac magnetic resonance scans straight from a registry of single ventricle patients, performs image classification, calculates bounding boxes, and segments the ventricles. The clinical utility of the pipeline is that there is very little human preprocessing required from the clinicians. The pipeline has great robustness as it is trained on multicenter data from different countries, with different scanners, image sizes and aspect ratios, patient ages (relating to heart sizes), and the inherent variability of single ventricle patients. Heart metrics calculated from our pipeline can guide treatment for single ventricle patients.

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Keywords