Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
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Abstract (arXiv)
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.
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References · 11
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
- 2019Deep learning for cardiac image segmentation: A reviewcited by 131
- 2019Deep Semantic Segmentation of Natural and Medical Images: A Reviewcited by 125
- 2021GAN-based Data Augmentation for Chest X-ray Classificationcited by 18
- 2018High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networkscited by 10
- 2019GAN-enhanced Conditional Echocardiogram Generationcited by 2
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