###### Abstract  
Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning. optical coherence tomography angiography; segmentation; classification; review; handbook +
Footnote †: journal: Medical Science  
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There are various algorithms that are employed to determine the final OCTA image with motion-contrast, also known as optical microangiography (OMAG) [4]. In OCTA imaging, the most popular algorithms use the OCT signal amplitude, the OCT signal phase, or both (also called complex amplitude). In particular, the split-spectrum amplitude-decorrelation angiography (SSADA) algorithm [5] was one of the first algorithms that was implemented within commercially available OCTA systems. Figure 2 depicts a block diagram example of an OCT system together with the signal processing unit to obtain OCTA A-scan signals and two example OCTA images. OCTA imaging presents many advantages when compared to other imaging modalities for vasculature, such as being quick and non-invasive, providing volumetric data that can allow the localization of pathology, and the ability to show both structural and blood flow information with a high resolution. Some of its current limitations include a relatively small field of view, a low penetration depth, and being prone to motion artefacts [1]. Hence, OCTA imaging is an ideal solution for a non-invasive quantitative analysis of superficial vasculature that does not cover too large of a surface area. In fact, the first clinical OCTA application is in ophthalmology, which is quite established in a clinical setting. In recent years, clinical applications of OCTA have also started branching out more as well, particularly for dermatological applications, which has recently been reviewed in [6]. As in numerous other medical imaging fields, there has been an extensive focus in recent years on quantifying and analyzing acquired OCTA images in an automatic or semi-automatic way to help physicians in making a diagnosis. This is known as the development of computer-aided diagnosis (CAD) systems. These systems aim to automatically extract quantitative information useful to clinicians or to automatically classify acquired images/volumes as healthy or pathological as a second opinion to experienced clinicians. There are numerous reviews in literature that focus on the clinical applications of OCTA imaging, especially when considering ophthalmology and specific diseases, such as, but not limited to, diabetic retinopathy (DR) [7; 8], age-related macular degeneration (AMD) [9] or glaucoma [10]. Other reviews found in literature focus on current and future clinical applications of OCTA imaging [3; 11; 12], to name a few. A couple recent studies focus on quantitative OCTA imaging, providing a nice overview of quantitative parameters that can be employed for artificial intelligence classification or comparing traditional and deep learning-based segmentation methods [11; 12], but both are still limited to ophthalmological applications and do not go into much detail about the various automated methods. Hence, a review and handbook focusing on the actual analysis methods, such as specific segmentation and classification techniques, is still lacking for OCTA imaging.