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The quantification of coronary artery calcium (CAC) score is a crucial metric for evaluating atherosclerotic plaque accumulation within coronary arteries, providing valuable predictive insights into coronary artery disease (CAD) events. Automated methods for CAC assessment using cardiac computed tomography (CT) have been extensively researched. However, a key aspect of these techniques is the accurate localization and segmentation of the heart within CT scans. This study presents a model-driven investigation into the efficacy of mediastinum segmentation, emphasizing its potential impact on CAC quantification in cardiac images. Utilizing a U-Resnet architecture, our methodology focuses on achieving a balance between model compactness and predictive capacity. The research involves two distinct datasets: one from a local medical institution and another serving as an external benchmark. The proposed model has demonstrated superior generalization in medical image segmentation exhibiting high accuracy in identifying true mask pixels within both its training and unseen datasets. Performance metrics showcase its success, with an average Dice Similarity Coefficient (DS) of 0.78, Precision (Pr) of 0.81, and Sensitivity (Se) of 0.81, surpassing the classical U-net model. Accurate localization and segmentation of the heart within CT images hold significant potential for facilitating the quantification of calcium deposits, contributing to the evaluation of coronary artery disease. Moreover, our research emphasizes the importance of recognizing potential labeling disparities in public datasets, attributing them to diverse labeling practices. In conclusion, this study highlights the model's potential in robust mediastinum segmentation, with direct implications for enhancing accuracy in CAC quantification within cardiac images. This contribution advances the field of accurate cardiac image analysis, ultimately benefiting patient care and enabling a more precise assessment of cardiovascular disease.
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