Two-Stage Convolutional Neural Network for Medical Noise Removal via Image Decomposition

Chang, Yi; Yan, Luxin; Chen, Meiya; Fang, Houzhang; Zhong, Sheng

doi:10.1109/tim.2019.2925881

Cited by 69 publications

(25 citation statements)

References 55 publications

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“…Compared with the existing linear and nonlinear filtering methods, the performance of the proposed improved Wiener filter in most noise models is relatively better. But the Wiener filter needs the spectrum information of the original signal and noise, and it can only achieve good results when the signal is sufficiently smooth [ 17 ]. The spatial domain denoising algorithm directly performs corresponding processing on the image pixels.…”

Section: Related Researchmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

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Section: Related Researchmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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“…The noise models beyond the Gaussian noise are also tested against the deep network model [21]. Dearling with more structured noise presented in the images is studied as well [22], [23]. In general, the supervised approach inevitably requires the available clean image set.…”

Section: Introductionmentioning

confidence: 99%

Denoising Across Data Acquisition Modalities for Mesoscopic Scale Optical Neuroimaging

Zhu

Han

et al. 2021

IEEE Access

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The ever-evolving mesoscopic scale optical imaging systems facilitate the new discoveries in the neuroscience research. They excel at providing a complete view of the long projections of a single neuron across the whole brain. Although the preparation protocols and the optical imaging systems are rapidly evolving, there are still some noise and artifacts interfering downstream data processing and analysis due to the defects in the imaging system or during the sample preparation. A specific denoising procedure is usually developed for one optical imaging system as a post-processing measure. However, the development of the optical imaging systems usually follows in an incremental manner. It would be better to adapt the denoising model to the new optical imaging system than training a denoising model from scratch. In this paper, the proposed learning schema and practice learn a new denoising model for a new optical imaging system based on the existing denoise models and bootstrap a denoising model without the ground-truth denoising labels. We achieve this through a CycleGAN based model and the fact that the optical imaging systems usually produce images both from the signal area and the fixture area. The experiments show that our proposed procedure can provide a comparable denoising performance against other state-of-the-art denoising methods for several optical neuroimaging datasets.

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“…Impressively, CNN-based denoising methods can be executed extremely efficiently (in milliseconds or seconds) once trained. Thus far, CNN-based denoising has been widely applied for various biomedical imaging modalities ranging from fluorescence microscopy 80,81 , optical coherence tomography 82 to x-ray imaging 83 , x-ray computed tomography 84 , PET 78,[85][86][87][88] and MRI 84,[89][90][91][92][93][94][95] .…”

Section: Introductionmentioning

confidence: 99%

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

Tian

Zaretskaya

Fan

et al. 2020

Preprint

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Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-millimeter isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ∼0.016, high whole-brain averaged peak signal-to-noise ratio of ∼33.5 dB and structural similarity index of 0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray–white surface placement, gray–CSF surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. The denoising performance is equivalent to averaging ∼2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan precision of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction sub-millimeter resolution.

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Two-Stage Convolutional Neural Network for Medical Noise Removal via Image Decomposition

Cited by 69 publications

References 55 publications

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Denoising Across Data Acquisition Modalities for Mesoscopic Scale Optical Neuroimaging

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

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