GANs for Medical Image Synthesis: An Empirical Study

Skandarani, Youssef; Jodoin, Pierre-Marc; Lalande, Alain

doi:10.3390/jimaging9030069

Cited by 103 publications

(28 citation statements)

References 29 publications

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“…Additionally, GANs have significant security issues, such as vulnerabilities that exploit the real-time nature of the learning process to generate prototype samples of private training sets [194]. Also, the use of deep neural networks such as CNN and GAN is limited by the need to have large annotated data sets, which is quite a challenge, especially in medicine [193]. All the above solutions are based on Euclidean space data that have fixed dimensions.…”

Section: Neural Network and Learning Algorithms In The Medical Image ...mentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

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Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

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Section: Neural Network and Learning Algorithms In The Medical Image ...mentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Image‐to‐image translation (e.g., for MR to CT, PET to CT) is a new possibility in medical image analysis, and recently MedGAN was proposed as a new end‐to‐end framework for medical image translation 93 . GANs have been shown to be very effective at synthesizing natural images, and expectedly they have found applications in medical image synthesis too 94 . Those approaches have not yet been applied to the field of ultrasound elastography.…”

Section: Deep Learning Technologiesmentioning

confidence: 99%

Deep learning in ultrasound elastography imaging: A review

Bhatt

et al. 2022

Medical Physics

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It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi‐static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.

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“…In addition, the situation of blood supply in lung cancer patients is complicated on CT imaging, which makes the generation of CECT more difficult. With the wide application of Generative Adversarial Networks (GAN) in image generation (Skandarani et al, 2023), these problems are expected to be solved. In recent years GAN has been widely used in medical image tasks such as image segmentation (Beji et al, 2023;Dash et al, 2023;Skandarani et al, 2023;Zhong et al, 2023), lesion classification (Chen et al, 2023;Fan et al, 2023), and lesion detection (Esmaeili et al, 2023;Vyas & Rajendran, 2023).…”

Section: Introductionmentioning

confidence: 99%

NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN

Hou,

Liu,

Zhang

et al. 2024

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Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training.Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.

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GANs for Medical Image Synthesis: An Empirical Study

Cited by 103 publications

References 29 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Deep learning in ultrasound elastography imaging: A review

NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN

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