Low-dose CT via deep CNN with skip connection and network-in-network

You, Chenyu; Yang, L.; Zhang, Yi; Wang, Ge

doi:10.1117/12.2534960

Cited by 38 publications

(25 citation statements)

References 17 publications

(15 reference statements)

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“…Chenyu You et al [24] proposed a deep denoising GAN based on a ResNet architecture with Wasserstein loss [23], [29]. The generator G proposed by the authors contains a Feature Extraction Network and a Reconstruction Network following an encoder/decoder model.…”

Section: B Denoising Gansmentioning

confidence: 99%

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

et al. 2021

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We propose a Generative Adversarial Network (GAN) optimized for noise reduction in CT-scans. The objective of CT scan denoising is to obtain higher quality imagery using a lower radiation exposure to the patient. Recent work in computer vision has shown that the use of Charbonnier distance as a term in the perceptual loss of a GAN can improve the performance of image reconstruction and video superresolution. However, the use of a Charbonnier structural loss term has not yet been applied or evaluated for the purpose of CT scan denoising. Our proposed GAN makes use of a Wasserstein adversarial loss, a pretrained VGG19 perceptual loss, as well as a Charbonnier distance structural loss. We evaluate our approach using both applied Poisson noise distribution in order to simulate low-dose CT imagery, as well as using an anthropomorphic thoracic phantom at different exposure levels. Our evaluation criteria are Peek Signal to Noise (PSNR) as well as Structured Similarity (SSIM) of the denoised images, and we compare the results of our method versus recent state of the art deep denoising GANs. In addition, we report global noise through uniform soft tissue mediums. Our findings show that the incorporation of the Charbonnier Loss with the VGG-19 network improves the performance of the denoising as measured with the PSNR and SSIM, and that the method greatly reduces soft tissue noise to levels comparable to the NDCT scan.

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Section: B Denoising Gansmentioning

confidence: 99%

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

et al. 2021

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“…There have been many outstanding works in the application of deep learning in the field of medical imaging [14]- [16]. In recent years, many supervised CNN methods have progressively developed retinal image classification [17]- [20] with the evolution of deep learning.…”

Section: A Retinal Image Classificationmentioning

confidence: 99%

Semi-Supervised Auto-Encoder Graph Network for Diabetic Retinopathy Grading

Zhang

Kang

et al. 2021

IEEE Access

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Diabetic Retinopathy (DR) causes quite a few blindness worldwide, which can be refrained by the timely diagnosis on retinal images. Recently, researches on deep learning-based retinal image classification have accelerated outstanding improvements in DR grading task. However, existing DR grading works are mostly limited to a supervised manner. They require accurately annotated data labeled by professional experts, and the annotating work is very laborious and time-consuming. We propose a Semisupervised Auto-encoder Graph Network (SAGN) for the challenging DR diagnosis to relax this constraint. Precisely, SAGN consists of three major modules: auto-encoder feature learning, neighbor correlation mining, and graph representation. Firstly, our model learns to extract representations from retinal images and reconstruct them as close to original inputs as possible. Then neighbor correlations among labeled and unlabeled samples are established by their similarities, calculated by the radial basis function. Finally, we operate Graph Convolutional Neural Network (GCN) to grade retinal samples from extracted features and their correlations. To evaluate the performance of SAGN, we conduct sufficient comparative experiments on APTOS 2019 dataset, trained from EyePACS. Results demonstrate that our SAGN model can achieve comparable performance with limited labeled retinal images with the help of large amounts of unlabeled data.

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“…The algorithm based on statistical learning has also been applied to CT reconstruction and achieved satisfactory reconstruction results, such as deep CNN reconstruction methods [30][31][32] and GAN based reconstruction methods [33][34] and so on. The reconstruction algorithm based on deep learning is also the method we will research.…”

Section: Discuss and Conclusionmentioning

confidence: 99%

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

Shen

Gong

et al. 2020

IEEE Access

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For limited-angle computed tomography (CT) image reconstruction, the classical total variation (TV) based algorithms suffer from the limited-angle artifacts, because TV only used the gradient information of the image. The priori image constrained compressed sensing (PICCS) based reconstruction algorithms can reduce the limited-angle artifacts by using a priori image consistent with the target image. However, it is difficult to ensure the consistency of priori image and target image in practice. In order to reconstruct high quality image when the prior image is inconsistent with the target image, we proposed a guided image filter reconstruction based on TV and prior image (TVPI-G). In each iteration phase, our algorithm first performs a TV step (include simultaneous algebra reconstruction technique (SART) and TV) to get initiatory reconstruction image; Then, the results of TV iteration are combined with prior images to form an intermediate result; Finally, we use the guided image filter to modify the intermediate results with the TV result as the guide image. Numerical reconstruction results on simulation phantom with different intensities Poisson noise illustrates that our proposed TVPI-G algorithm is better than other comparison algorithms in both qualitative and quantitative aspects, including TV, PICCS, and SART guided image filtering.

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Low-dose CT via deep CNN with skip connection and network-in-network

Cited by 38 publications

References 17 publications

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

Semi-Supervised Auto-Encoder Graph Network for Diabetic Retinopathy Grading

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

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