计算机科学
人工智能
光学相干层析成像
分割
联营
计算机视觉
模式识别(心理学)
图像分割
卷积(计算机科学)
棱锥(几何)
医学
人工神经网络
放射科
数学
几何学
作者
Xinyu Cao,Jiawei Zheng,Zhe Liu,Pingyu Jiang,Dengfeng Gao,Ran Ma
标识
DOI:10.1007/978-3-030-86365-4_48
摘要
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.
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