This is a single-center prospective, comparative, observational clinical trial conducted in the First Affiliated Hospital of Nanjing Medical University. The trial has been registered with the Chinese Clinical Trial Registry (http://www.chictr.org/cn/ registration number: ChiCTR-IPR-17014160). The study has followed the tenets of the Declaration of Helsinki, and the study protocol has been approved by the Ethics Committee of First Affiliated Hospital of Nanjing Medical University (2015-SR-150). Informed written consents have been obtained from all patients after explaining possible consequences of the study. For this initial study, the OCTA images are provided from authors of Ref. 6 with their consensus agreement. While the past study is solely discussing the feasibility of OCTA-based monitoring of neovascular regression on NVC after preoperative intravitreal conbercept injection, this work is strictly focusing on the image optimization algorithm itself.
5.2 The OCTA Imaging
For the OCTA imaging, AngioVue (version 2017.1.0.151; Optovue, Fremont, CA, USA) has been used to obtain split-spectrum amplitude decorrelation angiography. Two trained examiners (YS and LC) have performed the OCTA examinations after pupil dilatation using the incorporated B-follow-up eye-tracking model. The scanning area is centered on the NVC arising from the optic disc in 6 × 6 mm sections. The navigation line is dragged between retina and NVC, followed by setting the vitreous as reference to exclude retinal vessels underneath. Manual adjustment is occasionally needed to segment retina and NVC on the B-scan model. Low-quality images with signal strength index < 50, images with severe artifacts due to poor fixation, or undetectable images owing to NVC floating too high in the vitreous have been excluded in the analysis. All OCTA examinations have been performed three times for the mean values.
5.3 An improved VCA algorithm for OCTA microvascular extraction and quantification
Based on characteristics of the OCTA blood vessels, an improved blood vessel extraction and quantification method upon the VCA method is proposed with three major parts that include (1) pre-processing, (2) vessel extraction, and (3) vessel quantification. Figure 5 illustrates the three procedures and detailed step-by-step operations. For each processed OCTA microvascular image, a binary image that shows the microvasculars of original image with quantified microvascular parameters is obtained after applying the mentioned processing operations. The first pre-processing part includes image cropping and color space conversion. The next vessel extraction part includes operations of starting points detecting, vascular networks searching, binarization by automatic thresholding OSTU method, skeleton extraction of blood vessels, artifacts elimination, and vascular network merging. The final vessel quantification part includes quantification of the length and average width. The image processing software used in this study is MATLAB (version R2016a; MathWorks, Inc., Natick, MA, USA). The step-by-step operations are listed in the flowchart of Figure 6 with details explained next.
In the first pre-processing part, the input image is processed by a two-step process to ensure the subsequent operations simple and effective. The original image is cropped to the region of interest (ROI), and the color space is transformed to gray domain to reduce the computational complexity.
In the second vessel extraction part, the VCA method is first applied to determine the connected area from starting points of blood vessels as the microvascular network. In order to acquire an accurate and optimized vascular network set with lower noise and fewer artifacts, an improved VCA method is hereby proposed. The three main processing steps in the second part are hereby listed.
The first step is to identify the starting points of vascular network. The matrices of OCTA images are traversed by the partial line detection and the number of lines with no effective points are recorded. If proportion of the detected value is lower than the preset threshold, such a point is considered as a starting point. The Z-shaped traversal is then performed and continued to search other starting points of qualified vascular network until all traversal is completed.
The second step is to search all vascular networks connected with starting points. In the beginning, all starting points are marked as part of the initial vascular network and restored in the network point set. Then, we move the detection coordinate from starting points to the next position in the OCTA image, and calculate the minimum distance between the detected point to all points in the set. If is small enough (within 2 pixels), i.e., the detected point is close enough or connected with some vessels restored in the network point set, it will be marked as part of the initial vascular network and stored in the set. Through the global image traversal, all point sets that meet the requirements are stored and the initial blood vessel network is thus obtained. However, since the blood vessels extracted by the regional connectivity method are sensitive to the starting position, the original images will be rotated by 90°, 180°, and 270°, respectively, to search for different starting points and initial blood vessel. Finally, the vascular networks obtained from different starting points are merged to obtain a complete vascular network.
The last step is to optimize the initial microvascular network thus obtained. The binary microvascular image can be achieved by the OTSU image binarization method19, 20. In the initial vascular network, some noise and artifacts may be mistakenly marked as vessels due to the distance or gray value too close to the real vessels. Thus, we hereby propose to apply a noise and artifact reduction method, which combines the morphology and piece-by-piece analysis methods, into the VCA. Here, the skeleton of microvasculars in OCTA image is extracted to obtain a thinner vessel graph that presents the vascular skeleton only. The piece-by-piece analysis method is used to evaluate the correctness of extracted vascular skeleton for further noise and artifact reduction. The branch and breakpoint information of each blood vessel curve is used to obtain the branch length and total length of each blood vessel. If the branch length is too short, or the ratio of total length to the number of bifurcation points plus number of breakpoints is lower than the preset threshold, it will be considered as noise or artifacts for elimination. Accordingly, the noise and artifact pixels can easily be distinguished from vessel pixels. A completely optimized vascular network is finally obtained after execution of all mentioned steps.
In the third vessel quantification part with morphological characterization method, the length and average width of vessels are quantified. The total area (S) and length (L) of blood vessels are obtained through pixel accumulation from the vascular skeleton graphs, and the average width (W) of blood vessels are obtained following the indirect indices of mean trabecular plate thickness method21.