## 4 Discussion
When 3D volumes were analyzed, the scanning depth ranged from 1.2 mm to 3 mm. In this review, preprocessing methods for enhancing OCTA images and postprocessing methods for improving the segmentation or classification results were not taken into consideration. Preprocessing and postprocessing methods can improve segmentation and classification outcomes. This has been demonstrated both with traditional techniqueson OCTA images, such as thresholding [36], and with deep learning methods in digital pathology, which can also be extended to other research fields [95]. In OCTA imaging, the most commonly found preprocessing steps are those focusing on vessel enhancement. These filters aim to enhance structures within the image or volume that appear to have a vessel-like structure and reduce the signal if not. The most commonly used vesselness filter found in literature is the one proposed by [PERSON] et al., known as the Frangi filter [96]. This filter is characterized by a scale parameter that determines the dimensions of the vessels that are recognized and then enhanced in the image/volume. It is also possible to combine multiscale measurements (i.e., combine different scale parameter values) and hence recognize both smaller and larger vessels. Other common filters for vessel enhancement include the optimally oriented flux (OOF) filter [97], Gabor [98], and SCIRD-TS [99]. All of these filters also require parameter tuning similar to the Frangi filter. The next common preprocessing method is histogram normalization and contrast enhancement using methods such as CLAHE [100]. When considering 3D volumes, an important preprocessing method is projection artefact removal, a common OCTA artefact that causes the signal from a superficial vessel to protrude deeper within the volume than it should [101]. Numerous techniques for projection artefact removal have been proposed in literature [102]. One analyzed study combined stripe removal, another common artefact in OCTA images, with an active contour model [71]. Regarding segmentation postprocessing methods, the main techniques that were used were hole filling, small object removal and morphological operators to smooth the final boundaries. Another important factor to note is the difficulty of direct comparisons between studies. This can be observed when considering quantitative parameters obtained using different segmentation techniques, which has been accurately demonstrated for various thresholding methods [21; 45; 46], but can be extended to include any segmentation technique. Any segmentation method that is used will provide a different final binary image and therefore will change, even if only slightly, the obtained quantitative parameter. As mentioned previously, this calls for the dire need of a consensus across the research community for OCTA image quantification. This can be partially attributed to the fact that the majority of the studies that presented an automated technique for combined segmentation and classification using quantitative parameters did not actually validate the segmentation method against a manual segmentation but only validated the final classification results with a manual classification. Other studies that presented a segmentation technique did not actually validate the obtained segmentation but rather focused on the repeatability of the measurements, such as the studies by [21; 33; 41; 46], or on the statistical differences or correlation between quantitative parameters obtained on images from healthy and pathological subjects, such as the studies by [36; 42; 78]. Another comparison difficulty is simply the fact that almost all studies used proprietary databases. Fortunately, the open science movement has recently also reached OCTA imaging applications in the ophthalmological field, and a few recent studies provide not only a segmentation method for retinal OCTA images but also an open dataset. Specifically, [PERSON] et al.