Automated Quantitative Analysis of Blood Flow in Extracranial-Intracranial Arterial Bypass Based on Indocyanine Green Angiography.
Automated Quantitative Analysis of Blood Flow in Extracranial-Intracranial Arterial Bypass Based on Indocyanine Green Angiography.
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基于吲哚菁绿血管造影的颅外-颅内动脉搭桥血流自动定量分析
DOI：
10.3389/fsurg.2021.649719
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发表时间：
2021
影响因子：
1.8
通讯作者：
Gu Y
中科院分区：
文献类型：
--
作者：
Jiang Z;Lei Y;Zhang L;Ni W;Gao C;Gao X;Yang H;Su J;Xiao W;Yu J;Gu Y
基于吲哚青绿的微血管成像是外科医生进行颅内外动脉搭桥术的重要工具。在血液灌流方面，吲哚青绿图像包含了丰富的信息，这是人类或目前可用的商业软件无法有效解释的。本文提出了一种基于吲哚青绿视频的血流灌注评估自动处理框架，该框架包括三个阶段，即基于UNET深度神经网络的血管分割、基于尺度不变变换特征的术前和术后图像配准以及基于Horn-Schunck光流法的血流评估。这种自动处理流程可以显示任何血管的血流方向和强度曲线，以及手术前后的血液灌注量变化。本研究以嵌入显微镜的商用软件为参照，对该算法的有效性进行了评估。共有来自多个中心的120名患者参加了这项研究。对于血管分割，Dice系数为0.80，Jaccard系数为0.73。图像配准的成功率为81%。在术前和术后的视频处理中，自动处理方法与商业软件的符合率分别为89%和87%。该框架不仅实现了类似于商业软件的血液灌流分析，而且还可以在手术前后自动检测和匹配血管，从而量化血流方向，使外科医生能够直观地评估搭桥手术引起的血流灌注变化。
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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DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
影响因子：
4.8
作者：
Kalani, M. Yashar S.;Zabramski, Joseph M.;Spetzler, Robert F.
通讯作者：
Spetzler, Robert F.
使用拓扑血管树分割和分叉结构的视网膜图像配准
影响因子：
5.1
作者：
Chen, Li;Huang, Xiaotong;Tian, Jing
通讯作者：
Tian, Jing
影响因子：
4.8
作者：
Kamp, Marcel A.;Slotty, Philipp;Stummer, Walter
通讯作者：
Stummer, Walter
影响因子：
6.1
作者：
Patankar, Sanika S.;Kulkarni, Jayant V.
通讯作者：
Kulkarni, Jayant V.