Medical Image Synthesis with Deep Convolutional Adversarial Networks

By Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen. IEEE Transactions on Biomedical Engineering, 2018

562 citations recorded by the source corpus.

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 tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2b37777583372237e63405f258a1c40a3eda8bc9

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Citations in the corpus

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “…among the advanced data-enrichment techniques and are widely used for many applications in diverse domains, such as medical image synthesis [127], [128], compressive sensing MRI [129], [130], superresolution [130], security [131], classi-ﬁcation [132], image-to-image transformation [133], and…”
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks (2018, methodology): “Adversarial loss functions have recently been demonstrated for various medical imaging applications with reliable capture of high-frequency texture information [28]–[48].”
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, methodology): “Second, utilizing the adversarial strategy can push the generator to learn an inter-domain mapping and produce compelling target images [45] but there is a potential risk that the network may yield features that are not exhibited in target images due to the degeneracy of the mapping.”
Unsupervised Medical Image Translation With Adversarial Diffusion Models (2022, methodology): “Restrictions apply. a large number of subjects [23].”
Synthetic Data for Deep Learning (2019, methodology): “GANs have been widely applied to generating realistic medical images [40, 46,326,423,666,673].”
ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis (2021, methodology): “ing PTNet with mean-squared loss [57] and medSynth with mean-squared, adversarial and gradient-difference losses [32].”