## 4 Discussion
proposed a two-step unsupervised medical anomaly detection generative adversarial  
Figure 8: Sketching result of case 2: (**a**) original image, (**b**) doctor’s drawing, (**c**) R50 model, (**d**) R101 model, (**e**) AND model and, (**f**) OR model. network (MADGAN) which was composed of the reconstruction step via Wasserstein loss with Gradient Penalty + 100 \(\ell\)1 loss and the diagnosis step via \(\ell_{2}\) loss to discriminate subtle lesions on brain MRI in patients with early stage Alzheimer's disease and brain metastasis [20]. Its ability to identify diffuse lesions in multiple slices may play a role in recognizing multifocal chorioretian lesions on OCT images. There are still inevitable noises in synthesized images, which may affect the strategy of the selection of training models and need further study. Previous studies provided different deep-learning networks for automatic segmentation of retinal layers in OCT, in which the accuracy and practicability were found to be reliable. With respect to the use of deep-learning for depicting choroid, [PERSON] et al. provided a three-step method for measuring choroidal thickness, which combined morphological manipulation and deep-learning technique [21]: binarization and reconstruction of Bruch's membrane, choroid segmentation with CNN networks, measurement of distance between Bruch's membrane, and CSI. [PERSON] et al. used U-net to depict CSI [18]. They compared three networks including Cifar CNN (CNN 1), Complex CNN (CNN 2), and RNN networks for identifying boundaries of ILM, RPE, and CSI. Overall, the RNN model revealed the best results with a statistically significant lower error of 3.64 pixels in CSI localization, compared with Cifar CNN (error of 3.74 pixels) and Complex CNN (error of 3.97 pixels). In our study, we set 4 \(\upmu\)m per pixel, 400 \(\times\) 400 pixels per image. The choroid contour was defined with Bruch's Membrane and the choroid-scleral interface (CSI). An alternative deep-learning network other than CNN and RNN, which was composed of ResNet and FPN, was used. Four different methods of training models were utilized. However, the R50 model and OR model were significantly more accurate for depicting OCT choroid layer (errors of 4.85 pixels and 4.86 pixels, respectively) than the R100 and AND models (errors of 5.06 pixels and 5.04 pixels, respectively) as shown in Table 1. The increased tendency of overfitting accompanying the increasing training neural networks [22; 23] may be the reason why error conversely increased when more detailed training data were given. The utilization of unsupervised training models for deep neural network was provided to overcome the issue of overfitting [23; 24]. Table 1 provides information pertaining to average error and average execution time of different models, which allows physicians to choose the models they need for different clinical goals: OR model (R101 U R50) has higher sensitivity but is more time-consuming for screening pachyhoroid diseases. The AND model (R101 \(\cap\) R50) has the advantage of being time saving and has an acceptable error, so it can also be an alternative choice for clinical application. Mask-RCNN was used in our study, which multiplies feature channels rather than summation, providing faster and higher quality of choroid segmentation. A choroidal contour composed of continuous numerous points rather than a single point was acquired through this model, and the measurement of distance in various locations was available, which provided access to study peripapillary, subfoveal, or peripheral choroidal thickness. There were two limitations in this study. First, there was still the difficulty of accurately depicting the boundary of the choroid layer in the cases with a very thick choroid layer, even though we had used EDI-OCT, which provides more detailed images of the choroid layer without pupillary dilation compared to conventional SD OCT [25].