## 4 Discussion  
Many choroidal imaging biomarkers have been proposed in previous studies, from the boundaries of the choroid to the whole layer and components of the choroid, such as choroidal morphology, choroidal thickness, choroidal volume, thickness of choroidal vessel layer, and choroidal vascularity index [12]. This study attempted to develop a measurement method for choroidal thickness with greater convenience and accessibility. There are five components of the choroid layer, including Bruch's membrane, Haller's layer (the outer large vessel layer), Sattler's layer (the inner medium vessel layer), chorio-capillaris layer and suprachoroid [13]. Initially, we were unsure as to whether the Haller layer should be included as part of the choroidal thickness or not in consideration of the ambiguous boundary of the Haller's layer on OCT images. [PERSON] et al. compared the differences in the sublayers of choroid in intermediate AMD eyes with or without neovascular AMD in the fellow eye. They found significant thinning of Sattler's and Haller's layer in intermediate AMD with fellow eye nAMD, compared with healthy eyes and intermediate AMD without fellow eye nAMD [14]. [PERSON] et al. noted different patterns of pachyvessel in choriorefinal diseases with diffuse distribution of pachyvessel in CSCR and thick choroid PCV and focal distribution of pachyvessel in non-neovascular AMD, neovascular AMD, and conversely thin choroid PCV [15]. [PERSON] and associates noticed reduced choroidal vascularity index (CVI) of subfoveal Haller's layer as the initial presentation in diabetic patients before onset of diabetic retinopathy [16]. The importance of Haller's layer has been mentioned in multiple investigations, and we therefore included the Haller's layer in our study. Figure 7: Sketching result of case 1: (**a**) original image, (**b**) doctor’s drawing, (**c**) R50 model, (**d**) R101 model, (**e**) AND model, and (**f**) OR model. Various methodologies exist for depicting the CSI and measuring choroidal thickness; in addition, they can be divided into two types. The first is non-deep learning, which is image processing and semi-automatic. The disadvantage of the image processing methodology is its time-consuming nature, which limits its convenience for use in clinical practice. The second is the deep-learning method, which overcomes the confounding effect of the noise and artifacts in the previous image processing course. [PERSON] et al. presented CNNs combined with the graph search technique (CNN-GS) to distinguish the SD-OCT retinal layers of 39 non-exudative AMD patients with an error of 1.26 pixels between manual segmentation and CNN-GS methods [17]. [PERSON] et al. replaced CNN with RNNs (recurrent neural networks) for depicting OCT retinal layers [18]. Seven retinal layers of healthy children and three retinal layer boundaries (including inner limiting membrane, inner boundary of RPE and Bruch's membrane) were automatically segmented through RNNs, and the average errors were 0.53 pixels and 1.17 pixels, respectively. Unsupervised anomaly detection based on generative adversarial networks (GANs) is able to produce a synthesized image and discriminator to identify the synthesized image [5; 19]. They are independent and have their own learning state. Furthermore, GANs compete to enhance learning ability. GANs also have the benefit of being unmarked, which saves a lot of time, but hidden worries exist when used on medical images. This is because medical images should be labeled by physicians instead of using a discriminator to identify the synthesized image. Supervised learning learns from data provided by physicians, and these data are traceable for the standard answer of labeling. For rare diseases, data augmentation achieved via GAN can tackle the difficulty of collecting cases. GAN without \(\ell_{1}\) loss has been used to synthesize images of lung nodules with sufficient diversity [19]. [PERSON] et al.