The data management and image processing were performed on the same eye. We collected images of retinal fundus photography, OCT, and FA/ICGA from each eye. The flowchart of the image collection process is displayed in Figure 1. First, images were evaluated by the detection model to select and crop for different image types. The detection model, Cascade R-CNN [16], was trained with 599 images in different imaging modalities. The isolated images were first resized to 256 × 256 pixels. Subsequently, isolated images were augmented by slight adjustment of the brightness and contrast level, foggy masking, compression, rotation, horizontal flipping, and the addition of side lines. Then, 25 images were randomly selected from different imaging modalities and assembled. At least one image was required from each imaging modality. The assembled image package consisted of 25 segmented images from the same eye based on a combination of images with various augmentations and components of fundus retinal photography, OCT, and FA/ICGA. The size of the assembled images was 1280 × 1280 pixels, consisting of 25 images with a size of 256 × 256. Then, the image package was sent to the model for prediction.

Flowchart of multimodal image management and processing. OCT: optical coherence tomography; FA/ICGA: fluorescein angiography with or without indocyanine green angiography.

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