Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks
This addresses the challenge of obtaining pediatric CT data due to radiation risks, enabling better training for medical image processing tasks.
The paper tackled the scarcity of pediatric CT training data by proposing an age-conditioned ACGAN to synthesize realistic high-resolution CT images, enriching datasets for deep learning applications.
Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into account. The proposed network generated age-conditioned high-resolution CT images to enrich pediatric training datasets.