Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks

Huang, Zhenxing; Liu, Xinfeng; Wang, Rongpin; Chen, Jincai; Lü, Ping; Zhang, Qiyang; Jiang, Changhui; Yang, Yongfeng; Liu, Xin; Zheng, Hairong; Liang, Dong; Hu, Zhanli

doi:10.1016/j.neucom.2020.10.077

Cited by 27 publications

(11 citation statements)

References 35 publications

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“…trained a CNN to recover standard‐dose PET images with 6.25% ultralow‐dose 18 F‐fludeoxyglucose ( 18 F‐FDG) whole‐body PET images 19 . Several works based on generative adversarial networks (GANs) have also been used in this context 20 . Lu applied a GAN to recover whole‐body 18 F‐FDG PET images with an input consisting of 10% dose PET images 21 .…”

Section: Introductionmentioning

confidence: 99%

“…Even when PET images are collected from the same patient and reconstructed using the same algorithm, their noise levels can vary greatly due to the use of different scanners, scanning times and so on 10,26 . Besides, PET image noise levels may differ across individuals and medical devices 20 . For example, the injection doses of PET scans for children and the elderly are typically lower than those for adolescents.…”

Section: Introductionmentioning

confidence: 99%

“…19 Several works based on generative adversarial networks (GANs) have also been used in this context. 20 Lu applied a GAN to recover whole-body 18 F-FDG PET images with an input consisting of 10% dose PET images. 21 Lei et al proposed a Cycle GAN model to estimate the diagnostic quality of PET images using 1/8th count data.…”

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confidence: 99%

“…10,26 Besides, PET image noise levels may differ across individuals and medical devices. 20 For example, the injection doses of PET scans for children and the elderly are typically lower than those for adolescents. Therefore, it is often not enough to focus only on a specific noise level, but also more attention needs to be paid on the PET images with different noise levels.…”

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confidence: 99%

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Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Huang

Zhou

et al. 2023

Interdisciplinary Medicine

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Many deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Huang

Zhou

et al. 2023

Interdisciplinary Medicine

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“…At the same time, due to the far distance between the target and the sensor in the scene, there is obvious noise in the image due to the influence of atmospheric thermal radiation, and the target texture information is not prominent enough, and the visual effect of the image is poor. Image enhancement is an effective method to effectively improve image contrast, highlight image details and improve image visual effect [5,6] The image consists of pixels with different gray levels. Although point and line detection is very important in gray level discontinuity detection, edge detection is by far the most common method.…”

Section: Introductionmentioning

confidence: 99%

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Shen¹,

Jiang²,

Teng³

2018

ICST Transactions on Scalable Information Systems

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Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.

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Few‐shot segmentation framework for lung nodules via an optimized active contour model

Yang,

Shao,

Huang

et al. 2024

Medical Physics

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BackgroundAccurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge.PurposeExisting deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings.MethodsIn this paper, we propose a few‐shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high‐order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours.ResultsWe compared our proposed method with state‐of‐the‐art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators.ConclusionOur approach utilizes the output of few‐shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

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Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks

Cited by 27 publications

References 35 publications

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Few‐shot segmentation framework for lung nodules via an optimized active contour model

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