ArticleFrontiers in surgery2021
Automated Quantitative Analysis of Blood Flow in Extracranial-Intracranial Arterial Bypass Based on Indocyanine Green Angiography.
Article in Frontiers in surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- [Establishment and preliminary clinical validation of an artificial intelligence-assisted perforator identification model for anterolateral thigh perforator flaps based on ICGA videos].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2026Article
- Article
- Deep-Learning-Based Cerebral Artery Semantic Segmentation in Neurosurgical Operating Microscope Vision Using Indocyanine Green Fluorescence Videoangiography.Frontiers in neurorobotics · 2021Article
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Authors and funding
11 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Microvascular imaging based on indocyanine green is an important tool for surgeons who carry out extracranial-intracranial arterial bypass surgery. In terms of blood perfusion, indocyanine green images contain abundant information, which cannot be effectively interpreted by humans or currently available commercial software. In this paper, an automatic processing framework for perfusion assessments based on indocyanine green videos is proposed and consists of three stages, namely, vessel segmentation based on the UNet deep neural network, preoperative and postoperative image registrations based on scale-invariant transform features, and blood flow evaluation based on the Horn-Schunck optical flow method. This automatic processing flow can reveal the blood flow direction and intensity curve of any vessel, as well as the blood perfusion changes before and after an operation. Commercial software embedded in a microscope is used as a reference to evaluate the effectiveness of the algorithm in this study. A total of 120 patients from multiple centers were sampled for the study. For blood vessel segmentation, a Dice coefficient of 0.80 and a Jaccard coefficient of 0.73 were obtained. For image registration, the success rate was 81%. In preoperative and postoperative video processing, the coincidence rates between the automatic processing method and commercial software were 89 and 87%, respectively. The proposed framework not only achieves blood perfusion analysis similar to that of commercial software but also automatically detects and matches blood vessels before and after an operation, thus quantifying the flow direction and enabling surgeons to intuitively evaluate the perfusion changes caused by bypass surgery.
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