Abstract

Background: Myocardial perfusion scintigraphy (MPS) is widely used for noninvasive assessment of coronary artery disease, but publicly available datasets suitable for reproducible deep learning segmentation studies remain limited. This paper proposes CardioProto-SegNet, an image-only 3D anatomy-directed segmentation framework for myocardial region delineation using the public Myocardial Perfusion Scintigraphy Image Database v1.0.0 from PhysioNet, which contains 83 patient studies. Methods: The model is implemented as a 3D U-Net-like residual encoder–decoder network enhanced with squeeze-and-excitation channel recalibration and compact prototype-memory refinement at the bottleneck. Because the public dataset does not provide structured clinical variables, all reported results correspond to image-only myocardium segmentation. Results: Experimental evaluation demonstrated reliable segmentation performance on the available public dataset. CardioProto-SegNet achieved a Dice score of 0.7402 on the holdout test split. In five-fold cross-validation, the model obtained a mean Dice of 0.8239, mean IoU of 0.6870, mean accuracy of 0.9943, mean ROC-AUC of 0.9867, and mean PR-AUC of 0.8561. Since confirmed ischemia or infarction labels were not available, an exploratory image-derived subgroup analysis was additionally performed based on myocardial ROI uptake heterogeneity to examine model behavior in lower- and higher-heterogeneity cases. The ablation study showed that residual connections were important for stable segmentation performance, while the deeper variant achieved the highest tested performance, with a Dice score of 0.8290, IoU of 0.7096, and PR-AUC of 0.8831. Conclusions: Overall, the findings suggest that CardioProto-SegNet provides a reproducible public dataset benchmark for myocardium segmentation in MPS and may serve as a foundation for future downstream quantitative and CAD-oriented analysis when larger datasets with clinical labels become available.

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