Electronic cleansing in CT colonography using a generative adversarial network

Abstract

We developed a novel 3D electronic cleansing (EC) method for CT colonography (CTC) based on a generative adversarial network (GAN). GANs are machine-learning algorithms that can be trained to translate an input image directly into a desired output image without using explicit manual annotations. A 3D-GAN EC scheme was developed by extending a 2D-pix2pix GAN model to volumetric CTC datasets based on 3D-convolutional kernels. To overcome the usual need for paired input-output training data, the 3D-GAN model was trained by use of a self-supervised learning scheme where the training data were constructed iteratively as a combination of volumes of interest (VOIs) from paired anthropomorphic colon phantom CTC datasets and input VOIs from the unseen clinical input CTC dataset where the virtually cleansed output sample pairs were self-generated by use of a progressive cleansing method. Our preliminary evaluation with a clinical fecal-tagging CTC case showed that the 3D-GAN EC scheme can substantially reduce the processing time and EC image artifacts in comparison to our previous deep-learning EC scheme.