## 3 Results
### Segmentation Tasks
#### 3.1.2 Deep Learning  
Recently, the use of deep learning frameworks for analyzing medical images has seen an exponential growth. Deep learning implies the use of deep neural networks, which is an artificial neural network that has many layers between the input and output layer. Convolutional Neural Networks (CNNs) are specifically used in image analysis applications, as they apply numerous convolutions on the input image [45]. The main advantage of CNNs is that they can automatically learn high-level features and then provide a semantic segmentation by associating each pixel of the input image to a label or class. The drawbacks to deep learning methods are (a) the need of a large annotated database, which has somewhat, but not totally, been mitigated with the employment of transfer learning [46], (b) their complexity (i.e., requirement of an immense number of training parameters) and (c) the difficulty of interaction with any single layer of the network, which can contribute to the view of deep networks as black-boxes that do not explain their predictions in a way that is easily understandable by humans [47].  
All of the studies that employed deep learning techniques were based on ophthalmological applications, so either for FAZ segmentation or eye vascular segmentation. This can most likely be explained by the fact that larger databases are available for ocular applications, whereas the dermatological applications are still in the research stage and are not used on a daily basis in a clinical setting. The majority of the studies used already-known architecture styles with some modifications, such as the UNet [11; 48; 49; 50; 51; 52], VGG [53; 54; 55], and ResNet [13; 56], but two studies also employed custom-made networks [57; 58].  
The performance of the deep learning methods for eye vasculature segmentation was quite high, as demonstrated by the study by [PERSON] et al. [55] that employed a network that took as input the 3D acquired volume and then produced a 2D segmentation using a plane perceptron to enhance the perceptron ability in the horizontal direction. The authors obtained DSC values equal to 0.8941 with images with a 6 \(\times\) 6 mm\({}^{2}\) FOV, and equal to 0.9274 with images acquired on a 3 \(\times\) 3 mm\({}^{2}\) FOV. Another study that showed promising results was by [PERSON] et al. that first produced both an open dataset and also provided their source code [11]. Moreover, it provides an interesting comparison between deep learning techniques, specifically the UNet and CS-Net [59], and traditional methods. The best Dice coefficient was obtained using the deep learning methods (DSC = 0.89), yet the traditional adaptive thresholding method on filtered OCTA images also showed high Dice coefficient values (DSC = 0.86). Their study also emphasizes the importance of evaluating segmentation performance in terms of clinically relevant metrics [11]. When considering the FAZ determination, deep learning techniques also outperformed the other methods, as demonstrated by the study by [PERSON] et al. [60] that used a dataset of 405 images and a final DSC value equal to 0.9760. The study by [PERSON] et al. [61] also presented a deep learning method for CNV segmentation, with a maximum Intersection over Union (IoU) equal to 0.88.