scholarly journals Design and implementation of convolutional neural network architectures for low-dose CT image noise reduction

2021 ◽  
Author(s):  
Seyyedomid Badretale
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Low Dose ◽  
Ct Images ◽  
Learning Approaches ◽  
Low Dose Ct ◽  
Image Noise Reduction ◽  
Low Dose Computed Tomography

An essential objective in low-dose Computed Tomography (CT) imaging is how best to preserve the image quality. While the image quality lowers with reducing the X-ray dosage, improving the quality is crucial. Therefore, a novel method to denoise low-dose CT images has been presented in this thesis. Different from the traditional algorithms which utilize similar shared features of CT images in the spatial domain, the deep learning approaches are suggested for low-dose CT denoising. The proposed algorithm learns an end-to-end mapping from the low-dose CT images for denoising the low-dose CT images. The first method is based on a fully convolutional neural network. The second approach is a deep convolutional neural network architecture consisting of five major sections. The results of two frameworks are compared with the state-of-the-art methods. Several metrics for assessing image quality are applied in this thesis in order to highlight the supremacy of the performed method.

scholarly journals Design and implementation of convolutional neural network architectures for low-dose CT image noise reduction

2021 ◽  
Author(s):  
Seyyedomid Badretale
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Low Dose ◽  
Ct Images ◽  
Learning Approaches ◽  
Low Dose Ct ◽  
Image Noise Reduction ◽  
Low Dose Computed Tomography

An essential objective in low-dose Computed Tomography (CT) imaging is how best to preserve the image quality. While the image quality lowers with reducing the X-ray dosage, improving the quality is crucial. Therefore, a novel method to denoise low-dose CT images has been presented in this thesis. Different from the traditional algorithms which utilize similar shared features of CT images in the spatial domain, the deep learning approaches are suggested for low-dose CT denoising. The proposed algorithm learns an end-to-end mapping from the low-dose CT images for denoising the low-dose CT images. The first method is based on a fully convolutional neural network. The second approach is a deep convolutional neural network architecture consisting of five major sections. The results of two frameworks are compared with the state-of-the-art methods. Several metrics for assessing image quality are applied in this thesis in order to highlight the supremacy of the performed method.

scholarly journals Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising

2018 ◽  
Vol 4 (1) ◽  
pp. 297-300 ◽  
Author(s):  
Mattias P. Heinrich ◽  
Maik Stille ◽  
Thorsten M. Buzug
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Low Dose ◽  
Quantum Noise ◽  
Signal To Noise Ratio ◽  
Input Image ◽  
Convolutional Network ◽  
Low Dose Ct ◽  
Risk Of Cancer

AbstractLow-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions filters and a ResUNet that is trained with 10 convolutional blocks that are arranged in a multi-scale fashion. Both architectures feature a residual connection of the input image to ease learning. Training images are based on realistic simulations by using the XCAT phantom. The ResUNet approach shows the most promising results with a peak signal to noise ratio of 44.00 compared to ResFCN with 41.79.

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

scholarly journals Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

2021 ◽  
Vol 13 (19) ◽  
pp. 3859
Author(s):  
Joby M. Prince Czarnecki ◽  
Sathishkumar Samiappan ◽  
Meilun Zhou ◽  
Cary Daniel McCraine ◽  
Louis L. Wasson
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Precision Agriculture ◽  
Edge Computing ◽  
Training Data ◽  
Learning Approaches ◽  
Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

scholarly journals COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020 ◽  
Vol 7 ◽  
Author(s):  
Hayden Gunraj ◽  
Linda Wang ◽  
Alexander Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
Deep Convolutional Neural Network ◽  
Screening Tools ◽  
Screening Methods ◽  
Rt Pcr

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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