A Deep Learning-Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients.

Abstract

Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.

Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.

Study type: Retrospective.

Population: 427 patients with diverse cardiac conditions (305 non-ToF, 122 ToF), with 395 for training/validation, 32 ToF for internal testing, and 12 external ToF for generalizability assessment.

Field strength/sequence: Steady-state free precession cine sequence at 1.5/3 T.

Assessment: U-Net, Deep U-Net, and MultiResUNet were trained under three regimes (non-ToF, ToF-only, mixed), using manual segmentations from one radiologist and one researcher (20 and 10 years of experience, respectively) as reference, with consensus for discrepancies. Performance for LV, RV, and LV myocardium was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score, alongside regional (basal, middle, apical) and global ventricular function comparisons to manual results.

Statistical tests: Friedman tests were applied for architecture and regime comparisons, paired Wilcoxon tests for ED-ES differences, and Pearson's r for assessing agreement in global function.

Results: MultiResUNet model trained on a mixed dataset (TOF and non-TOF cases) achieved the best segmentation performance, with DSCs of 96.1% for LV and 93.5% for RV. In the internal test set, DSCs for LV, RV, and LV myocardium were 97.3%, 94.7%, and 90.7% at end-diastole, and 93.6%, 92.1%, and 87.8% at end-systole, with ventricular measurement correlations ranging from 0.84 to 0.99. Regional analysis showed LV DSCs of 96.3% (basal), 96.4% (middle), and 94.1% (apical), and RV DSCs of 92.8%, 94.2%, and 89.6%. External validation (n = 12) showed correlations ranging from 0.81 to 0.98.

Conclusion: The MultiResUNet model enabled accurate automated cardiac MRI segmentation in ToF with the potential to streamline workflows and improve disease monitoring.

Evidence level: 3.

Technical efficacy: Stage 2.