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Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images

2018 Tomography  
Abbreviations: Generative adversarial networks (GAN), deep convolutional GAN (DCGAN), magnetic resonance (MR), neuroradiologists (NRs), non-neuroradiologists (NNRs), positron emission tomography (PET),  ...  In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists.  ...  AI for Artificial Brain MRI  ... 
doi:10.18383/j.tom.2018.00042 fatcat:pvckdw6af5cypfkelszocyxwwy

Conditional Adversarial Network for Semantic Segmentation of Brain Tumor [article]

Mina Rezaei, Konstantin Harmuth, Willi Gierke, Thomas Kellermeier, Martin Fischer, Haojin Yang, Christoph Meinel
2017 arXiv   pre-print
Brain tumors segmentation has a special importance and difficulty due to the difference in appearances and shapes of the different tumor regions in magnetic resonance images.  ...  The recently proposed adversarial training has shown promising results in generative image modeling.  ...  The diversity of magnetic resonance imaging (MRI) acquisition regarding its settings (e.g. echo time, repetition time, etc.) and geometry (2D vs. 3D) also the difference in hardware (e.g. field strength  ... 
arXiv:1708.05227v1 fatcat:r4yk4p2tofdfrdtytsxwtrtxee

Segmentation of Suspicious Region using GAN Based CNN in Brain MR Images

2020 International Journal of Engineering and Advanced Technology  
In this paper, we realization on Generative Networks (GANs) for generating artificial multi-series attention Magnetic Resonance (MR) images.  ...  Generative Adversarial networks (GANs) are algorithmic architectures that use dual neural networks, pitting one in obstruction to the other (therefore the "opposing") with a intent to produce new, artificial  ...  INTRODUCTION There are positive difficult conditions in generating artificial many-sequence Brain Magnetic Resonance (MR) picture.  ... 
doi:10.35940/ijeat.d7688.049420 fatcat:ofv46evkjvbqzbfkdqh5eqdrky

Measuring Robustness in Deep Learning Based Compressive Sensing [article]

Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel
2021 arXiv   pre-print
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI).  ...  sensitive to small, yet adversarially-selected perturbations, (ii) may perform poorly under distribution shifts, and (iii) may fail to recover small but important features in an image.  ...  In: Magnetic Resonance in Medicine. 1990, pp. 192–225. [RFB15] O. Ronneberger, P. Fischer, and T. Brox. “U-net: Convolutional networks for biomedical image segmentation”.  ... 
arXiv:2102.06103v2 fatcat:57p7hrq2abeqbb4x2ufntj2274

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
MR-only radiotherapy planning 629 Multi-channel Generative Adversarial Network for Parallel Magnetic Resonance Image Reconstruction in K-space 632 Physics-based Simulation to enable Ultrasound monitoring  ...  Coupled Hidden Markov Models 668 Efficient Groupwise Registration for MR Brain Images via Hierarchical Graph Set Shrinkage 669 Conditional Generative Adversarial Networks for Metal Artifact Reduction  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

High-resolution medical image synthesis using progressively grown generative adversarial networks [article]

Andrew Beers, James Brown, Ken Chang, J. Peter Campbell, Susan Ostmo, Michael F. Chiang, Jayashree Kalpathy-Cramer
2018 arXiv   pre-print
(ROP), and multi-modal magnetic resonance images of glioma.  ...  Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensional space.  ...  This method can generate synthetic images of unprecedented size, and be used via its latent to space to learn imaging features in an unsupervised manner.  ... 
arXiv:1805.03144v2 fatcat:zcj53gvqkbgidfonkx5hpjrqrm

GAN-based synthetic brain PET image generation

Jyoti Islam, Yanqing Zhang
2020 Brain Informatics  
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).  ...  Han et al. proposed [70] a two-step GAN-based DA to generate and refine brain magnetic resonance (MR) images with/without tumors separately. Andreini et al.  ... 
doi:10.1186/s40708-020-00104-2 pmid:32232602 fatcat:fawsuivo7rcohpzq7ltypljc3i

Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy

Wen Li, Yafen Li, Wenjian Qin, Xiaokun Liang, Jianyang Xu, Jing Xiong, Yaoqin Xie
2020 Quantitative Imaging in Medicine and Surgery  
As compared to computed tomography (CT) images, magnetic resonance image (MRI) has high contrast for soft tissues, which becomes a promising imaging modality during treatment.  ...  Several deep learning network models were applied to implement this brain MRI synthesis task, including CycleGAN, Pix2Pix model, and U-Net.  ...  GAN, generative adversarial network; CT, computed tomography; MRI, magnetic resonance image. Figure 3 3 Schematic of the CycleGAN model.  ... 
doi:10.21037/qims-19-885 pmid:32550132 pmcid:PMC7276358 fatcat:p3ultej2dfb5npu6vuxghapueu

CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation [article]

Jonathan Rubin, S. Mazdak Abulnaga
2019 arXiv   pre-print
Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusion-weighted magnetic resonance imaging (DWI).  ...  Segmentation networks trained using generated CT-to-MR inputs result in at least some improvement on all metrics used for evaluation, compared with networks that only use CT perfusion input.  ...  Abstract-Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusionweighted magnetic resonance imaging (DWI).  ... 
arXiv:1904.13281v1 fatcat:pzrw6xrmfzfivk42smqa7ocsbq

Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans [article]

Mohammad Hamghalam, Baiying Lei, Tianfu Wang
2019 arXiv   pre-print
Specifically, generative adversarial network (GAN) is extended to synthesize high-contrast images.  ...  The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions.  ...  Following, we first introduce the synthetic image generator module, based on the generative adversarial networks (GANs) model [6] , and then 3D FCN architecture for segmentation will be discussed.  ... 
arXiv:1909.13640v1 fatcat:r6x5czkeafg2df2pbib7qsleai

FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution [article]

Mingfeng Jiang, Minghao Zhi, Liying Wei, Xiaocheng Yang, Jucheng Zhang, Yongming Li, Pin Wang, Jiahao Huang, Guang Yang
2021 arXiv   pre-print
In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super-resolution MR image from low-resolution magnetic resonance images, which  ...  Moreover, the spectral normalization process is introduced to make the discriminator network stable. 40 sets of 3D magnetic resonance images (each set of images contains 256 slices) are used to train the  ...  paper proposed a new method for super-resolution magnetic resonance images reconstruction by using fusion attention based generative adversarial networks (FA-GAN).  ... 
arXiv:2108.03920v1 fatcat:rkz46yus5vbhtbyjia4fqnlauu

Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks [article]

Jonas Denck, Jens Guehring, Andreas Maier, Eva Rothgang
2021 arXiv   pre-print
Therefore, we trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, image orientation).  ...  A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis.  ...  Introduction In Magnetic Resonance Imaging (MRI), multiple contrasts are usually acquired within a single exam that are required to make a reliable diagnosis.  ... 
arXiv:2102.09386v2 fatcat:o3wj6arm75ds7ef5vulfwsifsu

T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

Daisuke Kawahara, Yasushi Nagata
2021 Reports of Practical Oncology & Radiotherapy  
The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images.  ...  A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used.  ...  Generative adversarial networks proved superior in image generation [15] . Generative adversarial networks use two different networks of a generator and discriminator networks.  ... 
doi:10.5603/rpor.a2021.0005 pmid:33948300 pmcid:PMC8086713 fatcat:no635eihbfhhxogabdbexdydwq

Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements [article]

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio
2020 arXiv   pre-print
An idealized magnetic resonance (MR) imaging system and clinical MR brain images are considered.  ...  Generative adversarial networks (GANs) can be potentially useful to establish SOMs because they hold great promise to learn generative models that describe the variability within an ensemble of training  ...  An idealized magnetic resonance (MR) imaging system and clinical MR brain images from NYU fastMRI Initiative database 11 were considered.  ... 
arXiv:2001.09523v1 fatcat:vrijbshmgrb2ndys7rltww4bq4

Synthetic Magnetic Resonance Images with Generative Adversarial Networks [article]

Antoine Delplace
2020 arXiv   pre-print
In this work, we experiment three GAN architectures with different loss functions to generate new brain MRIs.  ...  Moreover, huge computation time is needed to generate indistinguishable images from the original dataset.  ...  Shakes Chandra for his useful insight on this project and for the helpful supervision he gave me when completing my Master thesis.  ... 
arXiv:2002.02527v1 fatcat:q6hypa4ixncarmsrbha3spprze
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