## 4 Discussion  
This is the first study to explore automated vessel segmentation in AO-OCT volumes. In this work, we found that augmentation of a UNet with a LSTM could significantly improve vessel segmentation performance with respect to AUC and recall on our held-out testing set when compared to a UNet alone. Figure 5: Sample segmentation for each architecture for each retinal layer (superficial vascular plexus (SVP), intermediate capillary plexus (ICP), deep capillary plexus (DCP)). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\# of Parameters (Million)**} & **Evaluation Time on 30 Slice** \\  & & **Image (Seconds)** \\ \hline UNet Only & 0.52 & 1.56 \\ LSTM-UNet & 1.82 & 184.79 \\ UNet-LSTM-UNet & 2.34 & 241.692 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of parameters and evaluation time for a 30-slice volume for each architecture. Previous studies have examined alternative 3D-2D vessel segmentation approaches in OCTA retinal imaging and other imaging modalities. [PERSON] et al., have developed a novel image projection network (IPN) which uses a unidirectional pooling layer to effectively learn weights for each slice within the projection step [43]. This unidirectional pooling layer in the IPN necessitates consistent pixel volumes as input, which would require interpolation or compression of non-uniform input for individual retinal layer blood vessel segmentation. In a large dataset of 500 OCTA volumes, their most recent iteration has shown a best Dice coefficient of 0.93 representing a 0.03 improvement over the baseline 2D-2D UNet with a Dice coefficient of 0.90 in their dataset [44]. As an imaging modality, OCTA uses motion-based processing to improve vessel contras [45] and it is expected that the segmentation performance on OCTA data would be greater in both the base UNet and the IPN than in AO-OCT volumes, which in its current form, does not perform any additional processing to improve vessel contrast. In comparison to the added benefit demonstrated by the 3D-2D architecture of [PERSON] et al. over baseline UNet, we achieve similar improvement of 0.04 with a Dice coefficient of 0.69 with our LSTM-UNet relative to 0.65 in the UNet alone. [PERSON] et al. developed a Spider U-Net, which similarly uses bidirectional convolutional LSTM to capture inter-slice connectivity, but employs the LSTM in between the encoding and decoding path of several UNet modules for each slice [46]. This architecture was trained and evaluated on multiple modalities, specifically Brain MRA, Abdomen CT and Cardiac MRI, for 3D-3D segmentation of blood vessels with Dice coefficients for Spider U-Net improving over 2D UNet by 0.05, 0.13, and 0.06, respectively for each dataset. While this architecture differs from ours in that it requires annotations for each slice within a 3D volume for training and was evaluated on (non-retinal) vessel and organ segmentation tasks, [PERSON] et al., found that incorporating a LSTM for inter-slice connectivity for their task produced fewer false-negative pixels. Our results are consistent with this finding as the recall for our LSTM-based models were significantly improved over the UNet based models alone. Our study also found that vessels in the ICP and DCP that are subject to shadowing artifacts evident in the raw image are more likely to be partially or fully segmented within the LSTM-based models compared to the UNet alone. This finding is expected, as an averaged projection would have lower pixel intensity and would be more difficult to distinguish in the 2D-2D approach, but in the inter-slice context gained from the LSTM could assist with identification of these vessels and could indicate that deeper plexuses may benefit from LSTM-based segmentation methods.