## 4 Metadata Analysis
### CNN Approach for Retina Segmentation in OCT Volumes  
[PERSON] et al. developed and evaluated a CNN approach for retina segmentation in OCT volumes (Figure 2) [89]. The innovative benefits of this system are its robustness and reliability in the presence of severe retinal pathology, as existing methods have been shown to perform poorly at this level [128]. In cases where the pathology is severe, the correct segmentation of the total retina needs a segmentation algorithm that does not overvalue the retinal structure and shape but takes into account its broad spatial context. Therefore, to meet these requirements, a CNN architecture was proposed in this study. The proposed algorithm performs a semantic segmentation, where each pixel is classified as belonging to the retina or background, rather than directly setting a segmentation boundary. This indirect method of segmenting the retina adds the flexibility needed to properly distinguish retinas as either healthy or pathological. Compared to other pixel classification algorithms, the variability in retinal structure and shape is easily captured by a large number of free parameters present in a typical CNN. To deal with large changes in the retina, it is necessary to include spatially distant information when determining the probability of a retinal pixel. If we consider only a small window around each pixel, the hypo-reflective content of a fluid-filled space can be confused with vitreous fluid or choroidal tissue. Thus, this study introduced the generalized U-net architecture that can include a large spatial context and provides a large receptive field, maintaining location accuracy [89]. The original U-net provides a \(140\times 140\) pixels receptive field. In this network, segmentation errors occur for certain pixels with large abnormalities since they do not have sufficient contextual information to differentiate them from the vitreous fluid or choroidal tissue [89; 129]. Briefly, the authors note that for the original network, with a receptive field of \(140\times 140\) pixels, segmentation errors occur within the fluid-filled abnormality because the lower/upper retina is not included in the receptive field, which gives the network the impression that the pixel is part of the choroid tissue or the vitreous fluid.  
Figure 1: Schematic representation of the bio-flow from photocurrent progression to the ganglion exit. The dual rod and cone pathways are mapping, in which the light signals are transformed into electrical signals that flow from the rod and cone cells through the individual pathways until these signals are integrated into the ganglion cells. Modified after [PERSON] et al. [35].  
They found that when increasing the receptive field to \(572\times 572\) pixels, this error did not occur because all abnormalities in the receptive field were included and enough contextual information was included to make a correct classification. The algorithm proposed in this study was compared to two reference algorithms, one provided by an OCT camera manufacturer and the other widely used in research environments, which is based on the segmentation of individual retinal layers to delimit the retina and obtain related measurements. Therefore, a large deviation in this layered structure (e.g., a fluid-filled space) results in errors in the segmentation outputs obtained by these algorithms [89].