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Purpose <p>This study proposes a novel algorithm framework for optical coherence tomography (OCT) and carotid angiography co-registration (cACR), aiming to improve diagnostic precision and treatment planning for carotid artery disease.</p> Methods <p>The OCT-cACR algorithm integrates an enhanced U-Net segmentation model and a marker detection algorithm for segmenting target carotid vessels and detecting OCT probe markers. Based on the segmented target region, the You Only Look Once (YOLO) algorithm is further utilized to detect and track the OCT probe marker. The acquisition time point of each frame served as the matching parameter to achieve registration between the two modalities. Following registration, an expert compared the identified marker position on each angiography frame with its corresponding actual location to measure the resulting geographical error. The accuracy of cACR was validated using four real clinical cases, with a geographical error of less than 0.35 mm as the evaluation criterion.</p> Results <p>The segmentation model achieved higher accuracy (Dice coefficient: 0.867 ± 0.166) compared to baseline U-Net models. OCT-cACR demonstrated an accuracy of 93.33 to 100% in four test cases, thus achieving precise alignment of angiography and OCT images.</p> Conclusion <p>The proposed cACR approach is feasible and accurate and may serve as a promising tool for improving the diagnosis and treatment of carotid artery diseases.</p>

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Automatic Image Co-registration of Carotid Angiography and Intravascular Optical Coherence Tomography Based on Machine Learning Method: A Pilot Feasibility Study

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Hui Xu,
  • Jia-Nan Li,
  • Yan Xu,
  • Jun-Ren Ma,
  • Kang Zong,
  • Rui Zhu,
  • Yi-Hui Cao,
  • Peng-Fei Yang,
  • Rui Zhao,
  • Jian-Min Liu

摘要

Purpose

This study proposes a novel algorithm framework for optical coherence tomography (OCT) and carotid angiography co-registration (cACR), aiming to improve diagnostic precision and treatment planning for carotid artery disease.

Methods

The OCT-cACR algorithm integrates an enhanced U-Net segmentation model and a marker detection algorithm for segmenting target carotid vessels and detecting OCT probe markers. Based on the segmented target region, the You Only Look Once (YOLO) algorithm is further utilized to detect and track the OCT probe marker. The acquisition time point of each frame served as the matching parameter to achieve registration between the two modalities. Following registration, an expert compared the identified marker position on each angiography frame with its corresponding actual location to measure the resulting geographical error. The accuracy of cACR was validated using four real clinical cases, with a geographical error of less than 0.35 mm as the evaluation criterion.

Results

The segmentation model achieved higher accuracy (Dice coefficient: 0.867 ± 0.166) compared to baseline U-Net models. OCT-cACR demonstrated an accuracy of 93.33 to 100% in four test cases, thus achieving precise alignment of angiography and OCT images.

Conclusion

The proposed cACR approach is feasible and accurate and may serve as a promising tool for improving the diagnosis and treatment of carotid artery diseases.