Naslov
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks

Autori
Habijan, Marija ; Galic, Irena

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Skup
7th International Conference on Smart and Sustainable Technologies (SpliTech)

Mjesto i datum
Bol, Hrvatska, 05.07.2022. - 08.07.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
conditional generative adversarial networks ; CT ; deep learning ; generative adversarial networks ; medical image generation ; unsupervised deep learning

Sažetak
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high attention. Artificial data augmentation and generation techniques such as generative adversarial networks (GANs) may help overcome this obstacle. In this work, we present an image generation approach that uses generative adversarial networks with a conditional discriminator where segmentation masks are used as conditions for image generation. We validate the feasibility of GAN-enhanced medical image generation on whole heart computed tomography (CT) images and its seven substructures, namely: left ventricle, right ventricle, left atrium, right atrium, myocardium, pulmonary arteries, and aorta. Obtained results demonstrate the suitability of the proposed adversarial approach for the accurate generation of high-quality CT images. The presented method shows great potential to facilitate further research in the domain of artificial medical image generation.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA