X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks

Ying, Xingde; Guo, Huang; Ma, Kai; Wu, Jian; Weng, Zhengxin; Zheng, Yefeng

doi:10.1109/cvpr.2019.01087

Cited by 180 publications

(135 citation statements)

References 39 publications

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“…This process is repeated when switching modalities even if the underlying anatomical structure is the same, resulting in a waste of human effort. Adversarial training, or more specifically unpaired cross modal-ity translation, enables reuse of the labels in all modalities and opens new ways for unsupervised transfer learning (Dou et al, 2018;Ying et al, 2019).…”

Section: Interesting Future Applicationsmentioning

confidence: 99%

Generative adversarial network in medical imaging: A review

Xin

Walia

Babyn

2019

Medical Image Analysis

1,354

787

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Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

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Section: Interesting Future Applicationsmentioning

confidence: 99%

Generative adversarial network in medical imaging: A review

Xin

Walia

Babyn

2019

Medical Image Analysis

1,354

787

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show abstract

“…However, a single view image led to much ambiguity due to the loss of depth information. Ying et al [ 16 ] designed an encoder-decoder framework to reconstruct CT volume from two orthogonal 2-D X-ray images and integrated it into an adversarial training process, named X2CT-GAN. The reconstruction accuracy was significantly improved compared [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…The adversarial training process of X2CT-GAN can be divided into generator part and discriminator part. As the conditional LSGAN is proved to have the best performance in [16], the loss function of the discriminator can be defined as…”

Section: Loss Design and Training Strategymentioning

confidence: 99%

“…In this paper, we propose a novel orthogonal-view 2-D/3-D rigid registration framework which integrates the deep-learning-based dense reconstruction with the GPU- accelerated 3-D/3-D registration, shown as Figure 1 . In the reconstruction stage, we follow the literature [ 16 ] to implement the dense reconstruction of a pseudo-CT as the target CT from two orthogonal views of radiographs. The dense reconstruction avoids the ill-posed nature and dimensional reduction of direct 2-D/3-D registration.…”

Section: Introductionmentioning

confidence: 99%

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Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery

Zhou

Liu

et al. 2021

Micromachines

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Intra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomography (CT) with annotated target information. Although this technique has been researched for decades, it is still challenging to achieve accuracy, robustness and efficiency simultaneously. In this paper, a novel orthogonal-view 2-D/3-D rigid registration framework is proposed which combines the dense reconstruction based on deep learning and the GPU-accelerated 3-D/3-D rigid registration. First, we employ the X2CT-GAN to reconstruct a target CT from two orthogonal fluoroscopy images. After that, the generated target CT and pre-operative CT are input into the 3-D/3-D rigid registration part, which potentially needs a few iterations to converge the global optima. For further efficiency improvement, we make the 3-D/3-D registration algorithm parallel and apply a GPU to accelerate this part. For evaluation, a novel tool is employed to preprocess the public head CT dataset CQ500 and a CT-DRR dataset is presented as the benchmark. The proposed method achieves 1.65 ± 1.41 mm in mean target registration error(mTRE), 20% in the gross failure rate(GFR) and 1.8 s in running time. Our method outperforms the state-of-the-art methods in most test cases. It is promising to apply the proposed method in localization and nano manipulation of micro surgical robot for highly precise MIS.

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“…Over time, artificial intelligence (AI) has come to play an important role in medical imaging tasks, including CT imaging [7], [8], magnetic resonance imaging (MRI) [9] and X-ray imaging [10]. Deep learning is a particularly powerful AI approach that has been successfully employed in a wide range of medical imaging tasks due to the massive volumes of data that are now available.…”

mentioning

confidence: 99%

COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network

Jiang

Chen

Loew

et al. 2021

IEEE J. Biomed. Health Inform.

138

102

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Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification.

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X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks

Cited by 180 publications

References 39 publications

Generative adversarial network in medical imaging: A review

Generative adversarial network in medical imaging: A review

Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery

COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network

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