Synthesizing Multi-Contrast MR Images Via Novel 3D Conditional Variational Auto-Encoding GAN

Yang, Huan; Lu, Xiaobo; Wang, Shuihua; Lu, Zhihai; Yao, Jian; Jiang, Yizhang; Qian, Pengjiang

doi:10.1007/s11036-020-01678-1

Cited by 22 publications

(17 citation statements)

References 30 publications

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“…In addition to fully CNN, there are other networks architectures that have been applied to learn the mapping from MR to CT images to generate pseudo CT with continuous values such as generative adversarial network [5][6][7][8][9], U-Net [10], residual U-Net [11], and HighRes3DNet [12].…”

Section: Related Workmentioning

confidence: 99%

“…However, the CT scan exposes the patient to radiation dose and generates images with low soft tissue contrast [4]. Recently, various learning based methods using deep learning have been proposed to learn the complex mapping from the tissue details of MR images to CT images in the same patients [5][6][7][8][9][10][11][12]. Another way to generate pseudo CT images is to segment MR images into different tissue classes.…”

Section: Introductionmentioning

confidence: 99%

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Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation

et al. 2022

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation

et al. 2022

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“…Pixelto-pixel [80] achieved image style transfer by training a conditional GAN whose generator and discriminator were both based on the input images. Many 3D medical applications take advantage of the conditional generative model, including conditional synthesis [33,109,140,148,215,219,220], segmentation [27,158,158,226,231], denoising [155,202,203,205,236], detection [59,147,188,208], and registration [11,154,230,234,238].…”

Section: Unconditional and Conditional Generative Modelsmentioning

confidence: 99%

3D Brain and Heart Volume Generative Models: A Survey

Liu¹,

Dwivedi²,

Boussaid³

et al. 2022

Preprint

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Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on Github to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey.

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“…The first section of this issue includes five papers, which focuses on the novel medical image processing methods, such as novel deep learning models, filters and classifications [6][7][8][9][10].…”

Section: Medical Image Processingmentioning

confidence: 99%