## 4 Discussion  
In the present paper, a novel algorithm, able to successfully delineate cysts' contours and motion artifacts on enface structural images of both SCP and DCP slabs, is described. Experiments have been conducted considering different images, in some cases affected by motion artifacts. The cysts present in the images were of different sizes and shapes, in order to ensure that the proposed methodology produces correct results in different scenarios. The instrument used in the present study can acquire images covering an area of 3 mm \(\times\) 3 mm or of 6 mm \(\times\) 6 mm. In both cases, the image has a size of 320 \(\times\) 320 pixels. Therefore, considering an area of 3 mm \(\times\) 3 mm ensures the acquisition of more details than capturing a 6 mm \(\times\) 6 mm area. For this reason, authors tested the proposed method on images covering a 3 mm \(\times\) 3 mm area. Moreover, a previous work [19] reported that the instrument used in this study is able to detect even early microvascular impairment. However, this algorithm can be easily applied to images of different sizes and obtained with different OCT-A instruments. In the literature, different approaches have been proposed for cyst detection on both OCT and OCT-A images. As for OCT, [PERSON] et al. [20] proposed a method based on the cascade application of retinal layer segmentation, median filtering, signal-to-noise ratio balancing, bilateral filtering, thresholding, boundary tracing and the rejection of false positives. The authors claimed to correctly delineate cyst borders. However, the authors validated their methodology with images acquired from only 16 patients [20], while in the present work we consider a larger dataset, which includes 194 images from 30 different patients. Finally, the authors did not report any result about computational complexity or processing time. Another interesting work was published by [PERSON] et al. [21]; in this paper, cyst detection was performed through watershed segmentation. In this case as well, the considered dataset included only 30 images [21]. Moreover, the computational complexity is not reported. A recent work by [PERSON] et al. adopted convolutional neural networks to delineate the cyst margin on OCT images [22]. The work considered three different datasets, acquired from four different commercial devices. The datasets were divided into training and test sets, each one including 15 training and 15 testing volumes. The authors reported some limitations related to the detection of small cysts. In fact, the convolutional neural network is not capable of detecting small cysts. It is also important to consider that the use of a convolutional neural network requires a labelled dataset and long training times. Finally, the computational complexity of the method is not reported. About all these works, it is important to highlight that they adopted OCT imaging, not OCT-A; therefore, a direct comparison with the proposed method cannot be performed. Figure 6: Enface structural OCT-A images with the edges found by the proposed method highlighted. In the third image from the left in the second row, it is possible to see that the motion artifact is not detected as cyst margins. As for OCT-A, [PERSON] et al. [12] were the first to try to differentiate intra-retinal cysts from non-perfused areas in SCP and DCP OCT-A images using a Spectral Domain instrument. Based on their results, they concluded that intra-retinal cysts can be seen as black oblong areas, completely devoid of signal, whereas non-perfused areas were described as dark regions with some internal noise and surrounded by capillaries [12]. In fact, all the images with retinal cysts evaluated in the present study have the characteristics described by [PERSON] et al. [12]. Therefore, the authors feel quite confident in excluding the presence of relevant ischemic areas in the macular region. [PERSON] et al.