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

    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 ...

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