Temporal phase coverage and patient cohort size in deep learning-based cardiac cine computed tomography angiography segmentation.

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.

RESULTS

Full cine training produced higher overall Dice (0.89 vs. 0.87) and lower overall HD95 (4.87 vs. 9.78 mm) than the single-phase reference (both p < 0.05). Right ventricular (RV) Dice was higher (0.91 vs. 0.84) and HD95 was lower (5.16 vs. 22.75 mm; both p < 0.05). The two-phase model achieved an overall Dice of 0.88 and an overall HD95 of 7.85 mm; neither metric differed significantly from the single-phase reference. HD95 generally increased with smaller cohorts, and both metrics deteriorated markedly in the five-patient configurations. Full cine training showed flatter phase-wise Dice profiles and lower across-phase variability. Between-subject volume differences exceeded phase-related within-subject differences for all structures (all p < 0.05).

CONCLUSION

In this cross-cohort comparison, full cine training showed higher overall Dice, lower overall HD95, and greater phase-wise consistency than single-phase training. The evaluated two-phase configuration may inform annotation-efficient cine CTA dataset design, but broader validation is needed.