## 3 Method
### Encoder-Decoder Architecture plus K-Means Clustering OCTA Segmentation Framework  
Encoder-decoder plus K-means clustering-based OCTA segmentation framework illustrates the framework adopted in this study, which is comprised of two main phases. (1) In phase 1, the first step is to divide the target image (ground truth image) into two parts, namely (1) FAZ and (2) RV. Then we convert the input image into grayscale format; these three images will be fed to phase 2 for training the DCNN encoder-decoder model. (2) In phase 2, we trained two models independently: Model 1 and Model 2 for RV and FAZ segmentation. Each model is comprised of an encoder-decoder network followed \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Number** & **Layer (Type)** & **Input Shape** & **Parameters** & **Trainable Parameters** \\ \hline
1 & Conv2D & [1, 1, 400, 400] & 60 & 60 \\
2 & ReLU & [1, 6, 200, 200] & 0 & 0 \\
3 & Conv2D & [1, 6, 200, 200] & 660 & 660 \\
4 & BatchNorm2D & [1, 12, 100, 100] & 24 & 24 \\
5 & ReLU & [1, 12, 100, 100] & 0 & 0 \\
6 & Conv2D & [1, 12, 100, 100] & 2616 & 2616 \\
7 & ReLU & [1, 24, 98, 98] & 0 & 0 \\
8 & Conv2D & [1, 24, 98, 98] & 10,416 & 10,416 \\
9 & ReLU & [1, 48, 96, 96] & 0 & 0 \\
10 & Conv2D & [1, 48, 96, 96] & 41,568 & 41,568 \\
11 & ReLU & [1, 96, 94, 94] & 0 & 0 \\
12 & Flatten & [1, 96, 94, 94] & 0 & 0 \\ Total parameters: 55,344; Trainable parameters: 55,344; Non-trainable parameters: 0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Encoder model detailed layer configuration. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Number** & **Layer (Type)** & **Input Shape** & **Parameters** & **Trainable Parameters** \\ \hline
1 & Unflatten & [1, 848256] & 0 & 0 \\
2 & ConvTranspose2D & [1, 96, 94, 94] & 41,520 & 41,520 \\
3 & ReLU & [1, 48, 96, 96] & 0 & 0 \\
4 & ConvTranspose2D & [1, 48, 96, 96] & 10,392 & 10,392 \\
5 & ReLU & [1, 24, 98, 98] & 0 & 0 \\
6 & ConvTranspose2D & [1, 24, 98, 98] & 2604 & 2604 \\
7 & BatchNorm2D & [1, 12, 100, 100] & 24 & 24 \\
8 & ReLU & [1, 12, 100, 100] & 0 & 0 \\
9 & ConvTranspose2D & [1, 12, 100, 100] & 654 & 654 \\
10 & BatchNorm2D & [1, 6, 200, 200] & 12 & 12 \\
11 & ReLU & [1, 6, 200, 200] & 0 & 0 \\
12 & ConvTranspose2D & [1, 6, 200, 200] & 55 & 55 \\ Total parameters: 55,261; Trainable parameters: 55,261; Non-trainable parameters: 0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decoder model detailed layer configuration. by a K-means clustering algorithm applied to each part to obtain the desired segmented output. Finally, the two segmented parts, predicated image 1 corresponding to the FAZ part and predicated image 2 corresponding to the RV part, are concatenated to obtain the final segmented output image of OCTA. * Phase 1. In this phase, the target image is separated into RV and FAZ components as shown in Figure 3, and the test image is then fed into phase 2. In the process of preparing the data for training, we extract the regions of interest for the RV and FAZ classes by identifying the corresponding pixel values of 255 and 100, respectively, based on the histogram of the ground truth data. * Phase 2. This phase involves feeding Model 2 (the FAZ green part), the test input image, and the FAZ part from the previous phase to train the network and predict the FAZ part and RV, respectively. A concatenation operator is used to combine the two images after predicting the two images independently to produce the final segmented output. We present the encoder-decoder architecture for OCTA segmentation in Figure 4. This architecture is then used in combination with the K-means clustering algorithm in the Encoder-decoder plus K-means clustering-based OCTA segmentation framework, which is shown in Figure 5 and provides the final output.