Frontiers in artificial intelligence
Temporal phase coverage and patient cohort size in deep learning-based cardiac cine computed tomography angiography segmentation
Door Heller A, Hsu LY, Sun AY en 3 anderen.
Inhoud in het Engels
Abstract
Background
Annotating all phases of cine computed tomography angiography (CTA) is burdensome. We evaluated how temporal phase coverage and patient cohort size affect deep learning-based cardiac segmentation.
Methods
Twelve 3D U-Net models were trained using a 110-patient single-phase CTA dataset or a 20-patient, 20-phase cine CTA dataset. Phase-coverage ablations used one, two, or 20 phases; patient-count ablations were performed within both datasets. One cine patient was used for validation and four independent patients were used for testing. Five cardiac structures were evaluated using Dice and the 95th-percentile Hausdorff distance (HD95). Phase-wise and patient-level Dice consistency and structure-specific volumetric variability within and between subjects were assessed.