## 4 Discussion  
In this review and handbook, we aimed to provide the reader with an overview of the most common segmentation and classification methods that are employed for automatic OCTA image or volume analysis. In this section, some key findings and future prospects are discussed. A first find is that the vast majority of studies (53 out of 56, 94.6%) focus on ocular applications, which can be explained by the fact that there are numerous clinical devices available for this specific field. The main clinical devices that were used in the analyzed studies were the: (a) Avanti OCTA system (Optovue, Inc., Fremont, CA, USA), (b) DRI OCT Triton or DRI OCT-1 Triton plus, (Topcon Medical Systems, Paramus, NJ, USA), and (c) PLEX Elite or Cirrus system (Carl Zeiss Meditec, Dublin, CA, USA). Three (5.4%) studies instead focused on the analysis of OCTA data acquired on human skin, two of which used custom-made laboratory OCT/OCTA systems [25; 41] and one of which employed a fiber-based swept-source polarization-sensitive OCT system (PSOCT-1300, Thorlabs) [76]. Hence, it can be observed how the use of OCTA imaging is quite established for ocular applications, but it is starting to move in other interesting directions, such as the non-invasive analysis of vasculature in skin. The fact that the upcoming research field of OCTA imaging is found in dermatology can be explained by the fact that the limited penetration depth of OCT/OCTA imaging makes the analysis of superficial vasculature an ideal application. A second important overall aspect to discuss is the type of data analyzed, either two-dimensional or three-dimensional. The acquired OCTA data from devices are inherently three dimensional, yet the vast majority of studies employ segmentation or classification methods on 2D images instead of the 3D volumes. The 2D images are typically obtained as a Maximum Intensity Projection (MIP) en face image of a specific retinal layer in the case of ocular applications, or of the entire acquired volume in the case of dermatological applications. A few recent studies have instead employed algorithms using the acquired volumetric data, in both ophthalmological and dermatological applications [27; 29; 36; 53]. To note is an interesting study by [PERSON] et al. [52] that employs a structure-constraint CNN architecture for a depth map estimation to map a segmentation obtained on 2D images into a 3D space. Especially when considering the up and coming research field of OCTA imaging in dermatology applications, the usage of the 3D volume should be considered preferable as it can provide an important 3D visualization of the vasculature and, more importantly, a more accurate vascular analysis and quantification [1]. A third overall aspect to take into consideration is the imaging area FOV. Considering a scan step size that is proportional to the FOV, the scan density for a smaller FOV (e.g., 1 \(\times\) 1 mm\({}^{2}\)) is higher than that for a larger FOV (e.g., 12 \(\times\) 12 mm\({}^{2}\)), providing a better scan resolution and hence a better ability to delineate detailed microvasculature. On the contrary, a larger FOV covers a wider area of scan coverage and is hence more likely to detect the presence or absence of pathological features such as non-perfusion and microa-neutrysms [94]. The FOV in the analyzed studies (not considering the depth which was not always reported) ranged from 1 \(\times\) 1 mm\({}^{2}\) up to 12 \(\times\) 12 mm\({}^{2}\). For ocular applications, most of the studies employed a FOV equal to 3 \(\times\) 3 mm\({}^{2}\) or 6 \(\times\) 6 mm\({}^{2}\), with only three studies employing a larger FOV and one study employing a smaller FOV. Interestingly, each of these four studies adopted either machine learning or deep learning techniques for segmentation and/or classification. For skin applications, the imaging FOV varied and was not consistent throughout the three analyzed studies, employing both a small FOV (i.e., 2.5 \(\times\) 2.5 mm\({}^{2}\)) and a larger FOV (i.e., 10 \(\times\) 10 mm\({}^{2}\)).