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Visual Computing for Industry, Biomedicine, and Art
Fig. 3 | Visual Computing for Industry, Biomedicine, and Art

Fig. 3

From: Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning

Fig. 3

Graphical representation of the proposed deep learning network. The proposed deep learning network included an encoder and decoder. The encoder comprised two Conv-BN- ReLu blocks (C1–C2) and five pooling blocks (P1–P5). The decoder comprised five upsampling blocks (U1–U5) and a reconstruction block (R1). The output of each layer is a three-dimensional feature map of size (h × w × d), where h and w are the height and width of the feature map, respectively, and d is the feature dimension

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