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Fig. 2 | Radiation Oncology

Fig. 2

From: Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study

Fig. 2

Our architecture diagram for the deep learning-based synthetic contrast-enhanced computed tomography (SCECT) generation model. The generator model has five Transition Down (TDG) and Up (TU) structures, and image features are analyzed in depth through Dense Block (DB) at each stage. Information of low and high-level features initially extracted from input image is preserved until the end through skip connection and concatenation. The inpuf of generator is NCT, ground truth is CECT, and predicted output is SCECT. The Discriminator model has four transition down (TDD) structures that are slightly different from TDG. The input of the discriminator is a two-channel image in which NCT is concatenated with CECT or SCECT, respectively. The ground truth is 0 or 1, and the predicted output is a decimal value between [0, 1]

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Radiation Oncology

ISSN: 1748-717X

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