(untitled) (International Journal of Innovative Computing, 2026)
Cardiovascular diseases remain among the leading causes of mortality worldwide. Cardiac fat, particularly epicardial adipose tissue and mediastinal adipose tissue, has increasingly been recognized as a key indicator in the early detection of cardiovascular diseases. However, this detection remains challenging due to the thin and often indistinct pericardial membrane that separates epicardial and mediastinal in non-contrast computed tomography (CT) images. Accurate segmentation of these fat regions and the pericardial membrane in CT imaging is essential to support clinical risk assessment and timely medical interventions. Traditional manual segmentation techniques have been employed in the past, but they are time-consuming and prone to inter-observer variability. Meanwhile, conventional machine learning algorithms often struggle to capture the complex spatial information inherent in tomographic CT images. To address these challenges, this study proposes an improved U-Net model designed to enhance the accuracy and adaptability of coronary fat segmentation in non-contrast CT scans. The model specifically addresses issues such as class imbalance, ambiguous labeling, and limited generalizability by incorporating a sigmoid activation function, which enables binary predictions for each anatomical structure and allows the model to effectively handle overlapping regions in multi-class segmentation tasks. The proposed model is evaluated on an annotated CT dataset using standard segmentation performance metrics. The results demonstrate that the advanced U-Net architecture developed in this study achieves improved accuracy in cardiac fat segmentation, thereby offering potential to enhance clinical diagnosis and support better patient treatment in medical imaging.