用机器学习自动分析冠状动脉光学成像，精准识别血管边界。
Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
- 先去噪去导丝伪影，再转坐标系，聚类提取特征
- 分类准确率达99.68%，各项指标最高达1.00
- 计算量小，几乎不用人工标注，适合临床实时应用
冠状动脉光学相干断层扫描（OCT）可实现高分辨率血管结构成像，但受噪声、成像伪影和复杂组织结构影响。本文提出一种基于机器学习的全自动血管分割与分类流程，整合图像预处理、导丝伪影去除、极坐标转直角坐标、无监督K均值聚类及局部特征提取。利用这些特征训练逻辑回归与支持向量机分类器，实现像素级血管分类。实验结果表明，该方法性能优异，精确率、召回率和F1分数最高达1.00，整体分类准确率达到99.68%。该方法在保持低计算复杂度的同时，实现精准血管边界检测，且只需极少人工标注。为自动化OCT图像分析提供了可靠高效方案，具备临床决策支持与实时医学图像处理的应用潜力。
原文摘要 · Abstract (English)
Intracoronary Optical Coherence Tomography (OCT) enables high-resolution visualization of coronary vessel anatomy but presents challenges due to noise, imaging artifacts, and complex tissue structures. This paper proposes a fully automated pipeline for vessel segmentation and classification in OCT images using machine learning techniques. The proposed method integrates image preprocessing, guidewire artifact removal, polar-to-Cartesian transformation, unsupervised K-means clustering, and local feature extraction. These features are used to train Logistic Regression and Support Vector Machine classifiers for pixel-wise vessel classification. Experimental results demonstrate excellent performance, achieving precision, recall, and F1-score values up to 1.00 and overall classification accuracy of 99.68%. The proposed approach provides accurate vessel boundary detection while maintaining low computational complexity and requiring minimal manual annotation. This method offers a reliable and efficient solution for automated OCT image analysis and has potential applications in clinical decision support and real-time medical image processing.
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