Improved U-Net for Plaque Segmentation of Intracoronary Optical Coherence Tomography Images

Optical coherence tomography (OCT) has been widely used in the assessment of coronary atherosclerotic plaques. Traditional machine learning methods are mainly based on the image texture features for the plaque segmentation. However, the texture features only represent the information of the local area, which may lead to unsatisfactory results. U-Net and its improved versions use continuous convolution and pooling to extract more advanced features, resulting in the loss of image spatial information and low plaque segmentation accuracy. This paper introduces a spatial pyramid pooling module and a multi-scale dilated convolution module into the U-Net to capture more advanced features while retaining sufficient spatial information. Based on our method, the F1 Score of the segmentation results of the four types of plaques including fibrosis, calcification, lipid and background are 0.85, 0.81, 0.80, 0.99, and the mIOU is 0.7663. Compared to other state-of-the-art methods, our method achieves better plaque segmentation accuracy.