scholarly journals A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuqin Li ◽  
Ke Zhang ◽  
Weili Shi ◽  
Yu Miao ◽  
Zhengang Jiang
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Superior Performance ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Automatic Feature Extraction ◽  
Adversarial Network ◽  
Image Context ◽  
Conditional Information ◽  
Denoised Image

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of PSNR = 33.2642 and SSIM = 0.9206 on JSRT datasets and PSNR = 35.1086 and SSIM = 0.9328 on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding.

scholarly journals An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shengnan Zhang ◽  
Lei Wang ◽  
Chunhong Chang ◽  
Cong Liu ◽  
Longbo Zhang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Evaluation Criteria ◽  
Structural Similarity ◽  
Block Matching ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Edge Preserving ◽  
Block Matching Algorithm ◽  
Adversarial Network

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.

scholarly journals Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

2021 ◽  
Vol 14 ◽  
Author(s):  
Eric Nathan Carver ◽  
Zhenzhen Dai ◽  
Evan Liang ◽  
James Snyder ◽  
Ning Wen
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Model Performance ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Generative Adversarial Network ◽  
Post Contrast ◽  
Adversarial Network ◽  
Fluid Attenuated Inversion Recovery

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.

scholarly journals MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis

2020 ◽  
Author(s):  
Yang Liu ◽  
Lu Meng ◽  
Jianping Zhong
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Generative Adversarial Network ◽  
Liver Ct ◽  
Adversarial Network ◽  
Computer Aided ◽  
Image Dataset ◽  
Aided Diagnosis

Abstract Background: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce.Methods: We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor.Results: The experiments showed that our method outperformed the other state-of-the-art methods, and can achieve a mean peak signal-to-noise ratio (PSNR) as 64.72dB.Conclusions: All these results indicated that our method can synthesize liver CT images with tumor, and build large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis.

scholarly journals A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

2020 ◽  
Author(s):  
Wenjie Liu ◽  
Ying Zhang ◽  
Zhiliang Deng ◽  
Jiaojiao Zhao ◽  
Lian Tong
Keyword(s):  
Machine Learning ◽  
Quantum Circuit ◽  
Machine Learning Algorithms ◽  
Generation Process ◽  
Generative Adversarial Network ◽  
Computing Platform ◽  
Adversarial Network ◽  
Quantum Machine Learning ◽  
Huge Impact ◽  
Conditional Information

Abstract As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human-computer interaction computing mode in cloud. The purpose of stabilizing the generation process and the interaction between human and computing process is achieved by inputting conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the ”input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

Iterative Regularization Denoising Method Based on OSV Model for Medical Image Denoising

2009 ◽  
Vol 36 (10) ◽  
pp. 2548-2551
Author(s):  
陈冠楠 Chen Guannan ◽  
陈荣 Chen Rong ◽  
林居强 Lin Juqiang ◽  
黄祖芳 Huang Zufang ◽  
冯尚源 Feng Shangyuan ◽  
...  
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Iterative Regularization ◽  
Denoising Method

An enhanced OCT image captioning system to assist ophthalmologists in detecting and classifying eye diseases

2020 ◽  
Vol 28 (5) ◽  
pp. 975-988
Author(s):  
Sivamurugan Vellakani ◽  
Indumathi Pushbam
Keyword(s):  
Short Term Memory ◽  
Clinical Decision ◽  
True Positive Rate ◽  
Eye Diseases ◽  
Age Related Macular Degeneration ◽  
Superior Performance ◽  
Image Captioning ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Age Related

Human eye is affected by the different eye diseases including choroidal neovascularization (CNV), diabetic macular edema (DME) and age-related macular degeneration (AMD). This work aims to design an artificial intelligence (AI) based clinical decision support system for eye disease detection and classification to assist the ophthalmologists more effectively detecting and classifying CNV, DME and drusen by using the Optical Coherence Tomography (OCT) images depicting different tissues. The methodology used for designing this system involves different deep learning convolutional neural network (CNN) models and long short-term memory networks (LSTM). The best image captioning model is selected after performance analysis by comparing nine different image captioning systems for assisting ophthalmologists to detect and classify eye diseases. The quantitative data analysis results obtained for the image captioning models designed using DenseNet201 with LSTM have superior performance in terms of overall accuracy of 0.969, positive predictive value of 0.972 and true-positive rate of 0.969using OCT images enhanced by the generative adversarial network (GAN). The corresponding performance values for the Xception with LSTM image captioning models are 0.969, 0.969 and 0.938, respectively. Thus, these two models yield superior performance and have potential to assist ophthalmologists in making optimal diagnostic decision.

scholarly journals Correction to “Noise Adaptation Generative Adversarial Network for Medical Image Analysis”

2020 ◽  
Vol 39 (7) ◽  
pp. 2566-2567
Author(s):  
Tianyang Miller ◽  
Jun Cheng ◽  
Huazhu Fu ◽  
Zaiwang Gu ◽  
Yuting Xiao ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Generative Adversarial Network ◽  
Adversarial Network
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