Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks. (October 2022)

• Record Type: Journal Article
• Authors: Amirrajab, Sina; Al Khalil, Yasmina; Lorenz, Cristian; Weese, Jürgen; Pluim, Josien; Breeuwer, Marcel
• Is Part Of: Computerized medical imaging and graphics. Volume 101(2022)
• Publication Date: 2022-10
• Subjects: Cardiac image synthesis -- Image simulation -- Semantic image synthesis -- Conditional GANs -- Image segmentation -- Cardiac MRI; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102123
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Synthesis of a large set of high-quality medical images with variability in anatomical representation and image appearance has the potential to provide solutions for tackling the scarcity of properly annotated data in medical image analysis research. In this paper, we propose a novel framework consisting of image segmentation and synthesis based on mask-conditional GANs for generating high-fidelity and diverse Cardiac Magnetic Resonance (CMR) images. The framework consists of two modules: i) a segmentation module trained using a physics-based simulated database of CMR images to provide multi-tissue labels on real CMR images, and ii) a synthesis module trained using pairs of real CMR images and corresponding multi-tissue labels, to translate input segmentation masks to realistic-looking cardiac images. The anatomy of synthesized images is based on labels, whereas the appearance is learned from the training images. We investigate the effects of the number of tissue labels, quantity of training data, and multi-vendor data on the quality of the synthesized images. Furthermore, we evaluate the effectiveness and usability of the synthetic data for a downstream task of training a deep-learning model for cardiac cavity segmentation in the scenarios of data replacement and augmentation. The results of the replacement study indicate that segmentation models trained with only synthetic data can achieve comparable performance to the baseline model trained with real data, indicating that the synthetic data captures the essential characteristics of its real counterpart. Furthermore, we demonstrate that augmenting real with synthetic data during training can significantly improve both the Dice score (maximum increase of 4%) and Hausdorff Distance (maximum reduction of 40%) for cavity segmentation, suggesting a good potential to aid in tackling medical data scarcity. Highlights: An optimized conditional GAN framework is proposed for cardiac MR image synthesis. Simulated data are utilized for training a multi-tissue segmentation network. Multi-tissue labels are important for increasing image quality and 3D consistency. The synthetic data can boost the performance of a DL segmentation network.