Convolutional auto-encoder for image denoising of ultra-low-dose CT

Nishio, Motohiro; Nagashima, Chihiro; Hirabayashi, Saori; Ohnishi, Akinori; Sasaki, Kaori; Sagawa, Tomoyuki; Hamada, Masayuki; Yamashita, Tatsuo

doi:10.1016/j.heliyon.2017.e00393

Cited by 84 publications

(48 citation statements)

References 26 publications

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“…It was inspired from a network designed for image super-resolution with three convolutional layers [21]. Convolutional auto-encoders have been used in [22]and [23] while the later also takes advantage of residual learning. All of the mentioned networks offer an end to end solution for low-dose noise removal.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

2019

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Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function do not suffer from over smoothing and blurring effects caused by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while only minimally changing the complexity of the network.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

2019

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“…The decoder learns to reconstruct the input as close as possible to the original using the latent representations. 3D-CAE extends this architecture by using convolutional layers that can extract features directly from 3D images [37][38][39] . The CAE has two main hyper parameters: the number of convolutional layers and the number of channels, which are the target of the current study.…”

Section: Convolutional Autoencoder Trainingmentioning

confidence: 99%

Three-dimensional convolutional autoencoder extracts features of structural brain images with a “diagnostic label-free” approach: Application to schizophrenia datasets

Yamaguchi

Hashimoto

Sugihara

et al. 2020

Preprint

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There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS), and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets including 71 SZ patients and 71 HS. The proposed 3D-CAEs were successfully reconstructed into high-resolution 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relevant clinical information. We explored the appropriate hyper parameter range of 3D-CAE, and it was suggested that a model with eight convolution layers might be relevant to extract features for predicting the dose of medication and symptom severity in schizophrenia.

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“…Recently, deep learning‐based generative algorithms have been shown to provide solutions for generative and translational problems in image processing including image denoising and image deblurring . These algorithms are primarily implemented as variational auto encoders (VAE) or generative adversarial networks (GAN).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep learning-based generative algorithms have been shown to provide solutions for generative and translational problems in image processing including image denoising and image deblurring. [22][23][24] These algorithms are primarily implemented as variational auto encoders (VAE) or generative adversarial networks (GAN). Generative deep learning methods have been used in the past for several applications also in medical imaging including generation of artificial computed tomography from MR for the purpose of radiation therapy planning and positron emission tomography attenuation correction.…”

Section: Introductionmentioning

confidence: 99%

Retrospective correction of motion‐affected MR images using deep learning frameworks

Küstner

Armanious

Yang

et al. 2019

Magnetic Resonance in Med

105

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Purpose Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion‐free reacquisition can become time‐ and cost‐intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a‐priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a‐priori knowledge, this problem is still challenging. Methods We propose the use of deep learning frameworks to perform retrospective motion correction in a reference‐free setting by learning from pairs of motion‐free and motion‐affected images. For this image‐to‐image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics. Results We observed that generative adversarial network‐based motion correction is feasible producing near‐realistic motion‐free images as confirmed by blinded subjective image quality assessment. Generative adversarial network‐based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error <0.08, structural similarity index >0.8, normalized mutual information >0.9). Conclusion Deep learning‐based retrospective restoration of motion artifacts is feasible resulting in near‐realistic motion‐free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.

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Convolutional auto-encoder for image denoising of ultra-low-dose CT

Cited by 84 publications

References 26 publications

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

Three-dimensional convolutional autoencoder extracts features of structural brain images with a “diagnostic label-free” approach: Application to schizophrenia datasets

Retrospective correction of motion‐affected MR images using deep learning frameworks

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