## 3 Results
### Segmentation Tasks
#### 3.1.1 Thresholding  
As can be noted from the large percentage of studies (n = 23, 48.9%), thresholding is the go-to method for segmenting structures of interest in OCTA images. Simply put, it is a method that marks all pixels that have an intensity lower (i.e., darker) or higher (i.e., brighter) than a specifically determined threshold as the object in the obtained binary image. How the intensity threshold is determined can vary greatly and can be divided into two main categories: global or local (also referred to as adaptive). Global thresholding determines one threshold value for the entire image frame and is determined by an analysis of the whole image intensity histogram. The Otsu method [17] is a commonly used automatic thresholding technique for OCTA images [18; 19; 20; 21; 22; 23] and is based on finding a threshold that minimizes the intraclass variance of the threshold  
Figure 4: Examples of analyzed segmentation methods and clinical segmentation tasks. Opthalmaglogical OCTA images are taken from the open ROSE dataset [13], except for the CNV segmentation task, taken from [16]. black and white pixels. Other global thresholding methods are based on finding a specific percentile of the image intensity histogram [24], the progressive weighted mean of the image intensity histogram [25; 26], or by simply fine-tuning a specific gray level [27]. Many analyzed studies employed a global thresholding technique without specifying exactly how the final threshold was determined [28; 29; 30; 31; 32; 33; 34]. Local, or adaptive, thresholding is based on analyzing the image in smaller areas, defined by a user-specified neighborhood. A threshold is therefore determined for each pixel, typically using first-order statistics, such as the mean and standard deviation of the pixel intensity within each considered neighborhood. The most commonly found local adaptive thresholding technique in OCTA images is the Phansalkar method [35] which was employed in numerous studies reported in this review [19; 34; 36; 37]. Importantly, [PERSON] et al. [38] provided an interesting outlook on using the Phansalkar thresholding technique for quantifying chorioapillaris, demonstrating the need of careful optimizing of the method's parameters for an accurate segmentation. Other common local thresholding methods used in OCTA images are the local mean [39] and local median [37; 40], and one study employed a signal-to-noise adaptive binarization method [41]. A couple studies used adaptive thresholding without specifying the exact method [30; 42]. Thresholding was the most common technique when considering the segmentation task of vasculature, both in ophthalmology and dermatology applications (see Table 1), but it is difficult to compare its performance with other techniques as the majority of the studies did not provide a quantitative validation of the vessel segmentation but rather either continued on to classify a specific disease or compared quantitative parameters computed on the segmentation (healthy vs. pathological subjects) or correlated the parameters with disease staging. The study by [PERSON] et al. [27] provided a quantitative validation of the obtained segmentation using global thresholding on optimally oriented flux filtered images, showing a Dice coefficient (DSC) equal to 0.8587 for healthy subjects, 0.8434 for proliferative diabetic retinopathy (PDR) subjects, and 0.8520 for severe non-proliferative DR (NPDR) subjects. Although the study was a rare one that employed 3D volumes instead of 2D en face images, the segmentation validation was performed on the 2D projections of the segmentation. Some other studies provided a segmentation comparison with a semi-automated segmentation, such as the one by [PERSON] et al. [25], and compared quantitative parameters obtained using the various segmentations (i.e., semi-automatic vs. automatic). This study also provided an intra-operator variability analysis, showing a high variability when using the semi-automatic software for segmentation.