GANs-Based Intracoronary Optical Coherence Tomography Image Augmentation for Improved Plaques Characterization Using Deep Neural Networks

Data augmentation using generative adversarial networks (GANs) is vital in the creation of new instances that include imaging modality tasks for improved deep learning classification. In this study, conditional generative adversarial networks (cGANs) were used on a dataset of OCT (Optical Coherence Tomography)-acquired images of coronary atrial plaques for synthetic data creation for the first time, and further validated using deep learning architecture. A new OCT images dataset of 51 patients marked by three professionals was created and programmed. We used cGANs to synthetically populate the coronary aerial plaques dataset by factors of 5×, 10×, 50× and 100× from a limited original dataset to enhance its volume and diversification. The loss functions for the generator and the discriminator were set up to generate perfect aliases. The augmented OCT dataset was then used in the training phase of the leading AlexNet architecture. We used cGANs to create synthetic images and envisaged the impact of the ratio of real data to synthetic data on classification accuracy. We illustrated through experiments that augmenting real images with synthetic images by a factor of 50× during training helped improve the test accuracy of the classification architecture for label prediction by 15.8%. Further, we performed training time assessments against a number of iterations to identify optimum time efficiency. Automated plaques detection was found to be in conformity with clinical results using our proposed class conditioning GAN architecture.