Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

Ma, Yuhui; Chen, Xinjian; Zhu, Weifang; Cheng, Xuena; Xiang, Dehui; Shi, Fei

doi:10.1364/boe.9.005129

Cited by 169 publications

(90 citation statements)

References 19 publications

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“…22 Intuitively, denoising was used as one of the strategies to reduce the irrelevancies of OCT scans since the noise of SDOCT scan impeded the medical analysis either visually or programmatically. 23 Thus, in experiment 1, we used a model based on ResNet blocks to compare the performance between the original and the irrelevancy reduced data. For denoising, we used nonlocal means 24 as the strategy, which performed both vertically (along x, z facets) and horizontally (along x, y facets) with different sets of parameters.…”

Section: Irrelevancy Reduction and Attention Mechanismmentioning

confidence: 99%

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

et al. 2019

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Section: Irrelevancy Reduction and Attention Mechanismmentioning

confidence: 99%

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

et al. 2019

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“…Further, we connected the generator input directly with the output of 16 residual blocks to further maintain the information from the input images. For other OCT GAN based denoising methods, Ma et al employed a U‐Net based structure for the generator design that is difficult to train a deep neural network without information loss. Huang et al and Chen et al used a DenseNet based generator network, which includes fewer training parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Generative adversarial network (GAN) is a promising deep neural network, which has been used in different machine learning problems, including text to image transformation , image generation and image editing . Recently, Huang et al , Ma et al and Chen et al employed the GAN method to denoise OCT images for ophthalmic applications. With different GAN network structures, more general datasets and better training strategies, the GAN method for OCT image denoising can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Zhang

Liu

et al. 2020

Journal of Biophotonics

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Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT denoising methods and other stateof-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

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“…An alternative, edge‐sensitive conditional generative adversarial network (cGAN) was reported efficient against speckle noise. This speckle noise reduction was demonstrated for OCT images which utilized an edge‐sensitive cGAN . Another implementation of the GAN approach with additional content loss metrics was proposed for simultaneous denoising and super‐resolution generation of optical coherence tomography .…”

Section: Deep Learning For Biophotonic Imagingmentioning

confidence: 99%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

Cited by 169 publications

References 19 publications

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Deep learning a boon for biophotonics?

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