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Jiang Z, Lei Y, Zhang L, Ni W, Gao C, Gao X, Yang H, Su J, Xiao W, Yu J, Gu Y. Automated Quantitative Analysis of Blood Flow in Extracranial-Intracranial Arterial Bypass Based on Indocyanine Green Angiography. Front Surg 2021; 8:649719. [PMID: 34179066 PMCID: PMC8225942 DOI: 10.3389/fsurg.2021.649719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/20/2021] [Indexed: 11/13/2022] Open
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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Affiliation(s)
- Zhuoyun Jiang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Liqiong Zhang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Chao Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiping Xiao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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