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

• URL: http://arxiv.org/abs/2507.00903v1
• Date: Tue, 01 Jul 2025 16:08:54 GMT
• Title: Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
• Authors: Andreea Bianca Popescu, Andreas Seitz, Heiko Mahrholdt, Jens Wetzl, Athira Jacob, Lucian Mihai Itu, Constantin Suciu, Teodora Chitiboi,
• Abstract summary: Tissue mapping enables quantitative cardiac tissue characterization but is limited by interobserver variability during manual delineation.<n>Traditional approaches relying on average relaxation values and single cutoffs may oversimplify complexity.<n>This study evaluates whether machine learning can achieve segmentation accuracy comparable to inter-observer variability.
• Score: 0.2593137041747032
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 variability, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether machine learning (ML) combining multiple features enhances disease detection. Materials & 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 left ventricle 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 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.

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