## 4 Discussion
[11] published the first open dataset of retinal parafrovel OCTA images with their associated ground truth manual segmentations, including a database of 55 ROIs from OCTA images acquired on 11 subjects. [PERSON] et al. [13] presented the ROSE dataset that contains 229 OCTA images with vessel annotations at either centerline-level or pixel level, and [PERSON] et al. [55] presented the OCTA-500 method and dataset which contains data acquired on 500 subjects with two FOV types. The dataset includes both OCT and OCTA volumes, six types of projections, four types of text labels, and two types of pixel-level labels. Very recently, a preprint by [PERSON] et al. [103] was made available that presents OCTAVA, an open-source toolbox for the quantitative analysis of optical coherence tomography angiography images. The authors present a Matlab GUI to help automate the quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow, including preprocessing, segmentation, and quantitative parameter computation steps. Thanks to these datasets and tools and the trend of making datasets and also automatic methods open for researchers to use, the problem of a lack of consensus should be mitigated in the coming years. Among the methods that presented a segmentation validation, from Table 1 it can be seen how the methods that employed a thresholding technique were mainly also those that did not present any segmentation validation, but rather focused the study on the analysis of specific parameters obtained from the segmentation with a clinical aspect. On the other hand, the other segmentation methods tend to include a validation of the segmentation and are more strictly focused on the presentation of a unique segmentation algorithm. When considering a complicated segmentation task, such as vasculature segmentation, the GGMRF models by [PERSON] et al. [69] and [PERSON] et al. [70] show very promising results, with a DSC equal to 0.95, but are limited to a database of slightly over 100 images. The more recent deep learning methods include much larger databases, such as the one presented by [PERSON] et al. [55] which includes 500 images and shows very promising results (DSC = 0.9274) when considering a 3 \(\times\) 3 mm\({}^{2}\) FOV. When considering easier segmentation tasks, such as the FAZ segmentation, it can be observed how the highest state-of-the-art segmentation results are reached only by deep learning methods, showing a 5-10% increase in segmentation performance parameters. From the methods analyzed in this review, it can be observed that machine learning methods are still the majority and also typically present the highest performance results for now, in terms of accuracy, when considering classification tasks. For example, for diabetic retinopathy classification, the highest accuracy was obtained by [PERSON] et al. [33], reaching a 98.5% accuracy on a database of 170 images using an SVM classifier. Still, the DcardNet presented by [PERSON] et al. [90] showed very similar, albeit slightly lower, results with a 96.5% accuracy on a dataset that was almost twice the size (303 images). Overall, what can be observed with both machine learning and deep learning classification methods is that, as the classification task increases in complexity (e.g., disease staging or multiple disease classification), the obtained classification results tend to decrease when using a similar-sized dataset, which can be expected. Quantitative OCTA imaging and the employment of automatic segmentation and classification methods is an emerging field, with a solid basis of various techniques for ophthalmological applications and the beginnings of a foundation of methods for dermatological applications.