## 2 Materials and Methods
### Model Training and Performance Evaluation  
AO-OCT volumes (\(n=177\)) were randomly split into training, validation, and testing datasets in a 60%-20%-20% split, respectively, ensuring an equal composition of SVP, ICP, and DCP volumes in each split. The characteristics of these splits are shown in Table 1. Each slice for each volume underwent automatic contrast adjustment following the Image) automatic contrast function that is based on histogram stretching [31]. To improve model robustness, we performed image augmentation [37] on the training dataset with random horizontal flip, vertical flip, affine transformation with translation and scaling, and random cropping to patches of 64 \(\times\) 64 pixels. All models were trained using a binary cross-entropy loss function. Convolutional and deconvolutional layer weights were initialized as described by [PERSON] et al. [38]. All models were trained for 600 epochs using an Adam optimizer at a learning rate of 0.0001. We also employed early stopping criteria based on validation set performance with a patience of 180 epochs. During evaluation, the model with the lowest binary cross-entropy loss on the validation set for each architecture was selected as the \"best model\" and used to segment volumes from each testing set in 64 \(\times\) 64 \(\times\) Ni pixel patches, where N was number of slices for a volume, i. The 64 \(\times\) 64 segmented masks were reassembled to form the final 2D mask output.  
We evaluated each model's performance on the testing set with an average Dice coefficient, area under receiver operating characteristic curve (AUC), precision, recall, and accuracy [39; 40; 41]. The metrics were compared between the models using a one-way ANOVA and follow-up Tukey test with statistical significance of differences determined to be \(p\)-value \(<0.05\). We also recorded the time it took each model to generate a segmentation for a single 30-slice volume. This volume was selected as it was the closest to average number of slices per volume within our testing set. All the algorithms were implemented on a computer with a NVIDIA GeForce RTX 2070 (8 GB) GPU and AMD Ryzen 5 3600 6-Core Processor @ 3.6 GHz (16 GB RAM). All image processing, model training, and model evaluation was performed in ImageJ and PyTorch 1.7.1 using Python 3 [31; 42].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Volumes**} \\ \hline  & Train & Validation & Testing & Total Volumes & Mean \(\pm\) Standard \\  & Train & Validation & Testing & Total Volumes & Deviation \# Slices per \\  & & & & & Volume (range) \\ Superficial Vascular Plexus & 43 & 13 & 13 & 69 & 34 \(\pm\) 20 \\  & Intermediate & & & & (range: 5–76) \\  & Capillary Plexus & & & & (range: 14–57) \\  & Deep Capillary Plexus & 26 & 8 & 8 & 42 & 33 \(\pm\) 11 \\  & & & & & (range: 12–60) \\ \hline Total Volumes & 109 & 34 & 34 & 177 & 33 \(\pm\) 15 \\  & & & & & (range: 5–76) \\ \hline Mean \(\pm\) Standard & & 33 \(\pm\) 15 & 32 \(\pm\) 13 & 31 \(\pm\) 15 & 33 \(\pm\) 15 \\  & (range) & (range: 5–76) & (range: 10–71) & (range: 5–60) & (range: 5–76) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive characteristics for adaptive optics optical coherence tomography volumes and image slices used for training, validation, and testing sets.