Jul 12, 2018

Medical Image Synthesis with Deep Convolutional Adversarial Networks

IEEE Transactions on Bio-medical Engineering
Dong NieDinggang Shen

Abstract

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 without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tas...Continue Reading

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Mentioned in this Paper

4-phenyl-3-furoxancarbonitrile
Strategy
Magnetic Resonance Imaging
Brain
Imaging @ Urinary System @ Magnetic Resonance Imaging (MRI)
Image Interpretation, Computer-Assisted
Imaging Techniques
3 Tesla Magnetic Resonance Imaging
Pelvis
Tomography, X-Ray Computerized Axial

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