ArticleAnnals of biomedical engineering2026
Automatic Image Co-registration of Carotid Angiography and Intravascular Optical Coherence Tomography Based on Machine Learning Method: A Pilot Feasibility Study.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis 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.
methodsThe 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.
resultsThe 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.
conclusionThe proposed cACR approach is feasible and accurate and may serve as a promising tool for improving the diagnosis and treatment of carotid artery diseases.
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