Denoising Diffusion Probabilistic Models (DDPMs) have attracted much attention as a powerful generative model and have been widely used in the field of computer vision. In the field of medical images, the noise contained in low-dose images often poses a challenge to deep learning methods, so we propose a Conjugate and Hybrid Diffusion Model (CH-DDPMs) for the task of low-dose fat CT image segmentation. CH-DDPMs is a kind of DDPMs integrated with a DnCNN module. It solves the problem of the noise interference in the low-dose fat CT images. CH-DDPMs achieves better results than other models in images with different noise levels, which can help doctors diagnose MetS more effectively.

Diabetes is one of the risk factors for metabolic syndrome and leads to an increased risk of cardiovascular events. Epicardial fat is located between the myocardium and the pericardium, directly contacting the coronary arteries, and is therefore closely related to cardiovascular events. We developed a method for segmenting epicardial fat, which achieved a DICE score of 0.6811 on the validation set. Based on this, we explored the correlation between epicardial fat, cardiac structure, and cardiac ejection function in diabetic patients. The results show that epicardial fat first alters the cardiac structure in diabetic patients, subsequently affecting their cardiac ejection function.