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.
Highlights
The superficial temporal artery to middle cerebral artery (STA-MCA), first reported by Spetzler and Martin in 1986 [1], has been widely used in blood flow reconstruction for cerebral hemorrhages or ischemia and complex intracranial aneurysms [2,3,4,5]
A microscope integrated with a near-infrared light-emitting device and a fluorescence acquisition system was aligned with the operating field (Figure 2A), and the indocyanine green (ICG) was diluted with 20 ml of isotonic saline and injected into patients through a peripheral intravenous bolus
UNet, SIFT, nearest neighbor search, and the HS optical flow method were innovatively integrated into the automatic blood flow quantitative analysis system
Summary
The superficial temporal artery to middle cerebral artery (STA-MCA), first reported by Spetzler and Martin in 1986 [1], has been widely used in blood flow reconstruction for cerebral hemorrhages or ischemia and complex intracranial aneurysms [2,3,4,5]. Raabe et al [6, 7] first demonstrated that the dynamic flow of indocyanine green (ICG) in anastomotic vessels can help in determining the patency and direction of intraoperative blood flow [8,9,10]. As an image postprocessing software, this software is unable to achieve continuous real-time and dynamic analysis of CBF and cannot quantify the flow direction of each blood vessel. It is clinically helpful to develop a fully automated quantitative blood flow analysis system that can be used to measure the real-time flow direction based on the original ICG-VA videos and to visually compare the difference in flow perfusion by correcting the images before and after the operation
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