## 3 Results
### Overview of Selected Studies
The 3D nnU-Net outperformed the other models, demonstrating superior performance across key metrics. It achieved a higher recall (sensitivity) of 0.77 \(\pm\) 0.22, accurately identifying approximately 77% of the lesion voxels. This exceeded the performance of the MD-GRU(0.60 \(\pm\) 0.31) and V-Net (0.61 \(\pm\) 0.25) models. The Dice coefficient for 3D nnU-Net was 0.78 \(\pm\) 0.13, indicating a notable degree of overlap between the predicted and manually annotated PCLs, outperforming MD-GRU (0.62 \(\pm\) 0.23) and V-Net (0.59 \(\pm\) 0.24). In terms of the Hausdorff distance, the 3D nnU-Net exhibited the lowest average maximum Hausdorff distance (315 \(\pm\) 172 \(\upmu\)m), indicating a high degree of alignment with the manually annotated lesion boundaries in comparison to MD-GRU (1542 \(\upmu\)m) and V-Net (2408 \(\upmu\)m). Additionally, the 3D model demonstrated a superior performance to its 2D counterpart in nearly all the metrics, providing more consistent predictions and reducing the gaps observed in the 2D model. This research presents a notable achievement in the automated segmentation of PCLs using OCT data, highlighting the potential of DL in ophthalmic imaging [26]. [PERSON] et al. [27] developed and evaluated a ML model for automated segmentation of UM, CN, and congenital hypertrophy of the retinal pigmented epithelium (CHRPE) using UWF fundus photography (Optos). A retrospective chart review was conducted at a tertiary academic medical center, including 529 lesion images from 479 patients, alongside 30 healthy controls. The dataset was divided into training (396 images) and testing (90 images) sets, with images based on superior quality and manually delineated lesion boundaries. The segmentation model was built using the DeepLabv3 architecture, employing pre-processing techniques such as image resizing and data augmentation. The images were resized to 500 \(\times\) 500 pixels with zero padding. Furthermore, data augmentation techniques, including horizontal flipping and slight rotation, were employed to enhance the dataset. The model was trained in over 400 epochs, with the final selection occurring at epoch 263 based on the lowest validation loss. The model's performance was assessed using the Dice coefficient, sensitivity, specificity, and confusion matrices, with evaluation conducted through lesion-based segmentation and image-based classification. The model achieved Dice coefficients of 0.86 for UM, 0.81 for nevi, and 0.85 for CHRPE, indicating high image-based segmentation accuracy. The sensitivity for lesion detection per image was 1.00 for UM, 0.90 for nevi, and 0.87 for CHRPE, while the specificity for images without lesions was 0.93 [27]. [PERSON] et al. [28] examined the effect of different color fusion strategies on a DL model for the automated classification of UM and CN. The dataset was retrieved from the University of Illinois at Chicago Eye Clinic and included 798 UWF retinal images from 438 patients, of whom 157 had UM, 281 had CN, and 360 had control images. Images from the eye that did not show any lesions were used as controls. The study used CNN architecture based on DenseNet121 with pre-trained weights derived from the ImageNet dataset for image classification. The model was trained by augmenting the data with horizontal/vertical flips and rotations to improve its generalization. The collected data were classified in two steps; in step 1, lesions were distinguished from normal images, and in step 2, the blebs were classified as either UM or CN. The study used color fusion techniques with monochrome models, where the individual color channels (red, green, blue) were evaluated independently for their performance.