Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.

Clinical Snapshot

Key Findings

• P Value: p < 0.001 for ML classification improvement

• Effect Size: Dice coefficient 85.4% for DL segmentation

• Primary Outcome: Segmentation accuracy measured by Dice similarity coefficient

• Nnt Or Sensitivity: F1-score 92.7% for random forest classification; sensitivity and specificity not reported

• Confidence Interval: Not reported

Clinical Application

Requires significant computational resources and technical expertise; integration into clinical workflow unclear Relevant to Australian cardiac imaging centres with MRI capability; would need TGA approval for clinical use; could support Medicare item 63460 (cardiac MRI) efficiency Patients undergoing cardiac MRI with T1/T2 mapping for suspected myocardial disease

Abstract

OBJECTIVES: Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation accuracy comparable to inter-observer agreement, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether machine learning (ML) combining multiple intensity-based statistical features enhances disease detection. MATERIALS AND METHODS: T1 and T2 maps were manually segmented. The test subset was independently annotated by two observers, and inter-observer variability was assessed. A DL model was trained to segment the left-ventricular blood pool and myocardium. Average (A), lower quartile (LQ), median (M), and upper quartile (UQ) were computed for the myocardial pixels and employed in classification by applying cutoffs or in ML. Dice similarity coefficient (DICE) and mean absolute percentage error evaluated segmentation performance. Bland-Altman plots assessed inter-user and model-observer agreement. Receiver operating characteristic analysis determined optimal cutoffs. Pearson correlation compared features from model and manual segmentations. F1-score, precision, and recall evaluated classification performance. Wilcoxon test assessed differences between classification methods, with p < 0.05 considered statistically significant. RESULTS: 144 subjects (mean age 42.2 years ± 16.1, 76 men) were split into training (100), validation (15) and evaluation (29) subsets. Segmentation model achieved a DICE of 85.4%, surpassing inter-observer agreement. Random forest applied to all features increased F1-score (92.7%, p < 0.001). CONCLUSION: DL facilitates segmentation of T1/ T2 maps. Combining multiple features with ML improves disease detection. KEY POINTS: Question Manual segmentation of myocardial T1/T2 maps is time-consuming and affected by inter-observer variability; relying on single cutoff values for diagnosis may oversimplify myocardial complexity. Findings Deep learning achieves segmentation accuracy within inter-observer agreement, while machine learning improves disease detection compared to singular cutoffs. Clinical relevance Automated segmentation and feature extraction from T1/T2 maps can enhance workflow efficiency, reduce inter-observer variability, and improve diagnostic consistency. The high recall of the machine learning model minimizes missed diagnoses, ensuring more reliable disease detection.

References

1. 1.Popescu, A. B., Seitz, A., Becker, M., Mahrholdt, H., Wetzl, J., Jacob, A., Itu, L. M., Suciu, C., & Chitiboi, T. (2026). Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection. European Radiology. https://doi.org/10.1016/j.ebiom.2024.105055