GAN-based synthetic brain PET image generation

• Published in: Brain informatics Vol. 7; no. 1; p. 3
• Main Authors: Islam, Jyoti, Zhang, Yanqing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 30.03.2020; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Alzheimer's disease; Artificial Intelligence; Brain; Brain imaging; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Computer vision; Datasets; Development and progression; Disease control; Diseases; Generative adversarial networks; Health Informatics; Image analysis; Image processing; Islam; Liquor; Machine learning; Medical imaging; Medical imaging equipment; Natural language processing; Neurosciences; Positron emission; Positron emission tomography (PET); Synthetic medical image generation; Technology; Time; Tomography; Generative adversarial networks; Positron emission tomography (PET); Brain imaging; Alzheimer’s disease; Synthetic medical image generation
• ISSN: 2198-4018; 2198-4026
• DOI: 10.1186/s40708-020-00104-2

In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer’s disease—normal control (NC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD).