Abstract
BACKGROUND: Coronary artery disease (CAD) manifests with a blockage the coronary arteries, usually due to plaque buildup, and has a serious impact on the human life. Atherosclerotic plaques, including fibrous plaques, lipid plaques, and calcified plaques can lead to occurrence of CAD. Optical coherence tomography (OCT) is employed in the clinical practice as it clearly provides a detailed display of the lesion plaques, thereby assessing the patient's condition. Analyzing the OCT images manually is a very tedious and time-consuming task for the clinicians. Therefore, automatic segmentation of the coronary OCT images is necessary. OBJECTIVE: In view of the good utility of Unet network in the segmentation of medical images, the present study proposed the development of a Unet network based on Sk-Conv and spatial pyramid pooling modules to segment the coronary OCT images. METHODS: In order to extract multi-scale features, these two modules were added at the bottom of UNet. Meanwhile, ablation experiments are designed to verify each module is effective. RESULTS: After testing, our model achieves 0.8935 on f1 score and 0.7497 on mIOU. Compared to the current advanced models, our model performs better. CONCLUSION: Our model achieves good results on OCT sequences.