Medical Image Synthesis with Deep Convolutional Adversarial Networks

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 12; pp. 2720 - 2730
• Main Authors: Nie, Dong, Trullo, Roger, Lian, Jun, Wang, Li, Petitjean, Caroline, Ruan, Su, Wang, Qian, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Adversarial learning; Artificial neural networks; auto-context model; Biomedical imaging; Brain - diagnostic imaging; Computed tomography; Computer Science; Datasets; Deep Learning; Generators; Humans; Image acquisition; Image generation; Image Interpretation, Computer-Assisted - methods; image synthesis; Magnetic Resonance Imaging; Medical imaging; Pelvis - diagnostic imaging; Radiation dosage; residual learning; Signal and Image Processing; State of the art; Synthesis; Target recognition; Task analysis; Therapeutic applications; Tomography, X-Ray Computed

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality...