Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

Bera, Sutanu; Biswas, Prabir Kumar

doi:10.1109/tmi.2021.3094525

Cited by 56 publications

(35 citation statements)

References 30 publications

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“…The original CT Liver image and denoised image is shown in Figure 2. [25]. A tumour segmentation method with a CNN model for CT image segmentation and denoising model is introduced.…”

Section: Figure 1 Stages Of Liver Tumormentioning

confidence: 99%

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CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Gavini¹,

Lakshmi²

2022

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Image denoising is an important concept in image processing for improving the image quality. It is difficult to remove noise from images because of the various causes of noise. Imaging noise is made up of many different types of noise, including Gaussian, impulse, salt, pepper, and speckle noise. Increasing emphasis has been paid to Convolution Neural Networks (CNNs) in image denoising. Image denoising has been researched using a variety of CNN approaches. For the evaluation of these methods, various datasets were utilized. Liver Tumor is the leading cause of cancer-related death worldwide. By using Computed Tomography (CT) to detect liver tumor early, millions of patients could be spared from death each year. Denoising a picture means cleaning up an image that has been corrupted by unwanted noise. Due to the fact that noise, edge, and texture are all high frequency components, denoising can be tricky, and the resulting images may be missing some finer features. Applications where recovering the original image content is vital for good performance benefit greatly from image denoising, including image reconstruction, activity recognition, image restoration, segmentation techniques, and image classification. Tumors of this type are difficult to detect and are almost always discovered at an advanced stage, posing a serious threat to the patient's life. As a result, finding a tumour at an early stage is critical. Tumors can be detected non-invasively using medical image processing. There is a pressing need for software that can automatically read, detect, and evaluate CT scans by removing noise from the images. As a result, any system must deal with a bottleneck in liver segmentation and extraction from CT scans. To segment and classify liver CT images after denoising images, a deep CNN technique is proposed in this research. An Image Quality Enhancement model with Image Denoising and Edge based Segmentation (IQE-ID-EbS) is proposed in this research that effectively reduces noise levels in the image and then performs edge based segmentation for feature extraction from the CT images. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better.

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“…The original CT Liver image and denoised image is shown in Figure 2. [25]. A tumour segmentation method with a CNN model for CT image segmentation and denoising model is introduced.…”

Section: Figure 1 Stages Of Liver Tumormentioning

confidence: 99%

“…Figure 2. CT liver original and denoised image CNN-based CT scans for organ localization and segmentation resulted in great accuracy and efficiency[25]. A tumour segmentation method with a CNN model for CT image segmentation and denoising model is introduced.…”

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confidence: 99%

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Gavini¹,

Lakshmi²

2022

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“…CT [50], [52], X-ray [12], to electron microscopy (EM) [22], [35]. The learning process of DNNs can be categorized into supervised [24], [40], [55] or unsupervised [2], [22], [57] approaches. Supervised learning DNNs consider clean and noisy image pairs for training where the noisy counterparts are obtained through adding synthesized noise to the target clean ones.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Distortion Learning for Medical Image Denoising

Ghahremani¹,

Khateri²,

Sierra³

et al. 2022

Preprint

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We present a novel adversarial distortion learning (ADL) for denoising two-and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a denoiser and a discriminator. The denoiser removes noise from input data and the discriminator compares the denoised result to its noise-free counterpart. This process is repeated until the discriminator cannot differentiate the denoised data from the reference. Both the denoiser and the discriminator are built upon a proposed auto-encoder called Efficient-Unet. Efficient-Unet has a light architecture that uses the residual blocks and a novel pyramidal approach in the backbone to efficiently extract and re-use feature maps. During training, the textural information and contrast are controlled by two novel loss functions. The architecture of Efficient-Unet allows generalizing the proposed method to any sort of biomedical data. The 2D version of our network was trained on ImageNet and tested on biomedical datasets whose distribution is completely different from ImageNet; so, there is no need for re-training. Experimental results carried out on magnetic resonance imaging (MRI), dermatoscopy, electron microscopy and X-ray datasets show that the proposed method achieved the best on each benchmark. Our implementation and pre-trained models are available at https: //github.com/mogvision/ADL.

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“…Recently, generative adversarial networks (GANs) have been proposed as a method to generate synthetic images to improve the existing oversampling techniques [ 7 ]. GANs, which are DL algorithms based on game theory, have been applied to several computer vision tasks such as image denoising, reconstruction, and, as mentioned, synthetic data generation [ 8 , 9 ]. Briefly, GANs consists of two competing actors: a generator and a discriminator.…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

Wang

Zhang

et al. 2022

Eur Radiol Exp

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Background Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). Methods We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training. Results Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data. Conclusions GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics.

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Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

Cited by 56 publications

References 30 publications

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN

Adversarial Distortion Learning for Medical Image Denoising

Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

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