CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy

Liu, Yanxia; Chen, Anni; Shi, Hongyu; Huang, Shu‐Chun; Zheng, Wanjia; Liu, Zhiqiang; Zhang, Qin; Yang, Xin

doi:10.1016/j.compmedimag.2021.101953

Cited by 84 publications

(41 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A benign tumor [ 27 ] can be formed anywhere on or in the patient's body when cells multiply more than they should or they do not die when they should [ 30 , 31 ]. Therefore, different machine learning techniques like logistic regression, naïve Bayes, and SVM [ 28 , 29 ] and deep learning techniques like CNN, RNN, and neural networks [ 32 , 33 ] are used in the field of healthcare for the detection purposes [ 34 , 35 ]. Multitask CNN is utilized to predict malignant subtypes in breast cancer tumors, and the accuracy rates of binary and quaternary classification at the patient level are 83.25 percent and 82.13 percent, respectively.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F 1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…The GAN and GAN-based networks that generate synthetic medical images have become very popular recently, as they solve the problem of limited availability of medical datasets. Liu et al [32] proposed a variant of Cycle GAN that uses the Pseudo Cycle consistent module and the domain control module to generate the Computed Tomography (CT) images. In this approach, the Pseudo Cycle consistent module controls the consistency of generated images, and the domain control module provides additional information of the domain.…”

Section: Gan Applications In Medical Imagingmentioning

confidence: 99%

Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN)

et al. 2021

View full text Add to dashboard Cite

Deep learning has gained immense attention from researchers in medicine, especially in medical imaging. The main bottleneck is the unavailability of sufficiently large medical datasets required for the good performance of deep learning models. This paper proposes a new framework consisting of one variational autoencoder (VAE), two generative adversarial networks, and one auxiliary classifier to artificially generate realistic-looking skin lesion images and improve classification performance. We first train the encoder-decoder network to obtain the latent noise vector with the image manifold’s information and let the generative adversarial network sample the input from this informative noise vector in order to generate the skin lesion images. The use of informative noise allows the GAN to avoid mode collapse and creates faster convergence. To improve the diversity in the generated images, we use another GAN with an auxiliary classifier, which samples the noise vector from a heavy-tailed student t-distribution instead of a random noise Gaussian distribution. The proposed framework was named TED-GAN, with T from the t-distribution and ED from the encoder-decoder network which is part of the solution. The proposed framework could be used in a broad range of areas in medical imaging. We used it here to generate skin lesion images and have obtained an improved classification performance on the skin lesion classification task, rising from 66% average accuracy to 92.5%. The results show that TED-GAN has a better impact on the classification task because of its diverse range of generated images due to the use of a heavy-tailed t-distribution.

show abstract

“…For example, GANs can combine a photo with an artist's style and then convert the image into an artist-style image [5]. This idea, together with the basis of GANs' ability to generate realistic-looking Magnetic Resonance Imaging (MRI) images, makes it possible for GANs to implement image conversion in medical imaging areas as well, such as the conversion of MRI and Computed Tomography (CT) [6].…”

Section: Introductionmentioning

confidence: 99%

Study for CT to MRI translation based on cycle-GAN and UNet-GAN

Lai

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

View full text Add to dashboard Cite

MRI and CT are both important medical imaging modalities, but MRI and CT imaging are done in different ways, each with its own advantages and disadvantages. Obtaining both images at the same time can help physicians make better decisions about treatment options. However, due to various limitations, some patients can only obtain one type of image. Therefore, it is necessary to find a well-performing GAN to transform MRI and CT images. In this paper, the effect of Cycle-GAN with different activation functions is compared, such as LeakyRELU, and different number of layers in MRI-CT conversion. Also, this article compares the effects of Cycle-GAN and UNet-GAN. The results indicate that the Cycle-GAN model using LeakyRELU as the activation function is better than the Cycle-GAN model using RELU as the activation function. Second, the effect of deepening the layers of the GAN model is worse than that of the base model. And the effect of UNet-GAN is similar to that of Cycle-GAN. This is not quite as expected, because Cycle-GAN has one more discriminator than UNet-GAN, and the effect should be better. But the experimental results do not confirm this conclusion.

show abstract

CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy

Cited by 84 publications

References 15 publications

[Retracted] Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

[Retracted] Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN)

Study for CT to MRI translation based on cycle-GAN and UNet-GAN

Contact Info

Product

Resources

About