5.1 Subjects
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 data acquired from two human subjects in vivo have been analyzed in this paper.
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 FVM arising from the optic disc in 6 × 6 mm sections. The navigation line is dragged between retina and FVM, followed by setting the Bvitreous as reference to exclude retinal vessels underneath. Manual adjustment is occasionally needed to segment retina and FVM 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 FVM 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 OCTA microvascular extraction and quantification
Based on characteristics of the OCTA blood vessels, an improved blood vessel extraction and quantification method utilizing the VCA method is proposed, which is composed of three parts including (1) pre-processing, (2) vessel extraction, and (3) vessel quantification. Figure 1 illustrates the three parts and the detailed operations in each step. For each OCTA microvascular image to be processed, a binary image that shows the microvascular of input image with quantified parameters of the microvasculars will be obtained after applying the various 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 width. The image processing software used in this study is MATLAB (version R2016a; MathWorks, Inc., Natick, MA, USA). The detailed operations in each part are described as following.
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 select the region of interest (ROI), and then the color space is transformed to gray domain to reduce the computational complexity.
In the second part, the VCA method is applied to determine the connected area from starting points of blood vessels as the microvascular network. In order to acquire the accurate and optimized vascular network sets with low noise and artifacts, an improved VCA method is hereby proposed. The main three processing steps in the second part are listed in the following.
The first step is to identify the starting points of vascular network. The matrices of OCTA images are traversed by performing the partial line detection, and the number of lines with no effective points are recorded. If the proportion of the value is lower than the preset threshold, this point is considered as a starting point. Then, the Z-shaped traversal is 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 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 network point set. If is small enough (within 2 pixels), which means the detection 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 network point 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 are rotated by 90°, 180°, and 270° to search for different starting points and initial blood vessel respectively. Finally, the vascular networks from different starting points are merged and a complete vascular network is obtained.
The third step is to optimize the obtained initial microvascular network. The binarized microvascular image can be obtained by the OTSU image binarization method18, 19. In the initial vascular network, parts of the noise and artifacts could be marked as vessels due to their distance and gray value being close to real vessels. Thus we propose to apply the noise and artifacts reduction method into VCA, which is combined with the morphology and piece-by-piece analysis method. In this step, the skeleton of the microvascular in the OCTA image is extracted to obtain a thinner vessel graph which presents the vascular skeleton only. For further noise and artifacts reduction, a piece-by-piece analysis method is used to evaluate the correctness of extracted vascular skeleton. The branch and breakpoint information of each blood vessel curve can be used to obtain the branch length as well as 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 artifacts pixels can easily be distinguished from vessel pixels. A completely optimized vascular network is finally obtained after execution of all previous steps.
In the third part of further morphological characterization method, the length and width of vessels will be quantified. The total area (S) and length (L) of blood vessels are obtained through pixel accumulation from the vascular skeleton graphics, and the average width (W) of blood vessels can be obtained following indirect indices of mean trabecular plate thickness method20.