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Deep learning is a popular and powerful tool in computed tomography (CT)image processing such as organ segmentation, but its requirement of largetraining datasets remains a challenge. Even though there is a large anatomicalvariability for children during their growth, the training datasets forpediatric CT scans are especially hard to obtain due to risks of radiation tochildren. In this paper, we propose a method to conditionally synthesizerealistic pediatric CT images using a new auxiliary classifier generativeadversarial network (ACGAN) architecture by taking age information intoaccount. The proposed network generated age-conditioned high-resolution CTimages to enrich pediatric training datasets.

Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks