МЕТОД ІНТЕГРАЦІЇ ДОМЕННИХ ЗНАНЬ НА ОСНОВІ ГРАФОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ СЕГМЕНТАЦІЇ ЗОБРАЖЕННЯ МРТ СЕРЦЯ

While deep learning has significantly advanced the automated analysis of cardiac magnetic resonance imaging (MRI), a persistent gap remains between high-performance research prototypes and their practical deployment in clinical workflows. In this work, we present a comprehensive, knowledge-integrated method designed to bridge this gap by holistically addressing these operational demands. The proposed method introduces a multi-stage, standards-aware pipeline that transforms raw medical images into auditable, clinically relevant insights. The process begins with a standardized data ingestion module for DICOM and NIfTI formats, ensuring robust de-identification and canonical spatial orientation. Segmentation of cardiac structures is performed by a 3D volumetric model (SKIF-Seg), which is subsequently exported to the ONNX format. This guarantees cross-platform inference portability via ONNX Runtime, enabling consistent performance across diverse computational environments. Crucially, the method moves beyond pixel-level prediction by extracting clinically established measurements, such as ventricular volumes, myocardial mass, and ejection fraction, from the segmentation masks. These metrics are structured as nodes in a knowledge graph, where edges explicitly encode established anatomical and physiological relationships. A Graph Convolutional Network (KI-GCN) then reasons over this structured representation to draw a conclusion, directly integrating domain knowledge into the decision-making process. Evaluated on the public ACDC and M&Ms-2 datasets, our approach achieves competitive segmentation performance with macro-Dice scores of 0,939 ± 0,021 and 0,927 ± 0,025, respectively. The subsequent diagnostic module delivers robust classification results, attaining a macro-ROC-AUC of 0,964 ± 0,018. The key conclusion is that by synergizing domain knowledge with rigorous software engineering practices, our method yields a portable, interpretable, and well-calibrated system that significantly closes the gap between algorithmic performance and true clinical reliability.