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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords