Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

Huang, Zhenxing; Chen, Zixiang; Quan, Guotao; Du, Yuzhe; Yang, Yongfeng; Liu, Xin; Zheng, Hairong; Liang, Dong; Hu, Zhanli

doi:10.1109/trpms.2022.3150322

Cited by 20 publications

(7 citation statements)

References 66 publications

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“…However, other network structures are also promising, such as GANs, which have demonstrated superior performance in generative image modeling cases 35,36 . In addition, some semi‐supervised and unsupervised methods are also used to solve this problem 37 . Therefore, we will consider incorporating noise priors into other types of network architectures in future work.…”

Section: Discussionmentioning

confidence: 99%

“…35,36 In addition, some semi-supervised and unsupervised methods are also used to solve this problem. 37 Therefore, we will consider incorporating noise priors into other types of network architectures in future work. In addition, our method is based on a 3D structure, which aims to preserve the spatial information contained in PET images.…”

Section: The Impacts For Inaccurate Noiselevel Estimationmentioning

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: Discussionmentioning

confidence: 99%

Section: The Impacts For Inaccurate Noiselevel Estimationmentioning

confidence: 99%

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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“…[22][23][24][25] Convolutional neural networks (CNN) are one of the most representative deep learning algorithms. [26][27][28][29] CNN can automatically extract features by learning a large amount of data and have thus been widely applied in the field of image segmentation. Long et al 30 proposed a fully convolutional network, in which the last fully connected layer was replaced with a convolutional layer.…”

Section: Segmentation Methodsmentioning

confidence: 99%

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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“…The performance of MDAM in testsets of the FDA and AAPM is shown in Tables 1 and 2. We also test the performance of wavelet, BM3D, CNN17-L2, RED-CNN, VGG , SMGAN-2D, and WGAN [37][38][39] on the above test set. It can be seen that for the two test datasets, image quality evaluation metrics of tested methods exhibit similar characteristics.…”

Section: Performance Comparisonmentioning

confidence: 99%

Transfer learning framework for low‐dose CT reconstruction based on marginal distribution adaptation in multiscale

et al. 2023

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Background: With the increasing use of computed tomography (CT) in clinical practice, limiting CT radiation exposure to reduce potential cancer risks has become one of the important directions of medical imaging research. As the dose decreases, the reconstructed CT image will be severely degraded by projection noise. Purpose: As an important method of image processing, supervised deep learning has been widely used in the restoration of low-dose CT (LDCT) in recent years. However, the normal-dose CT (NDCT) corresponding to a specific LDCT (it is regarded as the label of the LDCT, which is necessary for supervised learning) is very difficult to obtain so that the application of supervised learning methods in LDCT reconstruction is limited. It is necessary to construct a unsupervised deep learning framework for LDCT reconstruction that does not depend on paired LDCT-NDCT datasets. Methods: We presented an unsupervised learning framework for the transferring from the identity mapping to the low-dose reconstruction task, called marginal distribution adaptation in multiscale (MDAM). For NDCTs as source domain data, MDAM is an identity map with two parts: firstly, it establishes a dimensionality reduction mapping, which can obtain the same feature distribution from NDCTs and LDCTs; and then NDCTs is retrieved by reconstructing the image overview and details from the low-dimensional features. For the purpose of the feature transfer between source domain and target domain (LDCTs), we introduce the multiscale feature extraction in the MDAM, and then eliminate differences in probability distributions of these multiscale features between NDCTs and LDCTs through wavelet decomposition and domain adaptation learning. Results: Image quality evaluation metrics and subjective quality scores show that, as an unsupervised method, the performance of the MDAM approaches or even surpasses some state-of -the-art supervised methods. Especially, MDAM has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection. Conclusions: We demonstrated that, the MDAM framework can reconstruct corresponding NDCTs from LDCTs with high accuracy, and without relying on any labeles. Moreover, it is more suitable for clinical application compared with supervised learning methods.

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Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

Cited by 20 publications

References 66 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

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

Transfer learning framework for low‐dose CT reconstruction based on marginal distribution adaptation in multiscale

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