1. Articles from Yanye Lu

    1-10 of 10
    1. Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography

      Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography

      Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the ...

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    2. Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography

      Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography

      Purpose: Optical coherence tomography angiography (OCTA) is a premium imaging modality for non-invasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: 1) the angiogram information extraction is only limited to the locally consecutive B-scans; 2) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood-information-fused Pseudo-3D U-Net ...

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    3. Automated Analysis of Choroidal Sublayer Morphologic Features in Myopic Children Using EDI-OCT by Deep Learning

      Automated Analysis of Choroidal Sublayer Morphologic Features in Myopic Children Using EDI-OCT by Deep Learning

      Purpose: The purpose of this study was to analyze the choroidal sublayer morphologic features in emmetropic and myopic children using an automatic segmentation model, and to explore the relationship between choroidal sublayers and spherical equivalent refraction (SER). Methods: We collected data on 92 healthy children (92 eyes) from the Ophthalmology Department of Peking University First Hospital. The data were allocated to three groups: emmetropia (+0.50 diopters [D] to -0.50 D), low myopia (-0.75 D to -3.00 D), and moderate myopia (-3.25 D to -5.75 D). We performed standardized optical coherence tomography (OCT) and developed ...

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    4. Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images

      Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images

      As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice. In this paper, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only ...

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    5. Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography

      Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography

      Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation ...

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    6. Weakly Supervised Deep Learning Based Optical Coherence Tomography Angiography

      Weakly Supervised Deep Learning Based Optical Coherence Tomography Angiography

      Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this paper, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation ...

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    7. N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semi‐supervised deep learning

      N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semi‐supervised deep learning

      Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal‐to‐noise ratio (SNR) and high‐resolution (HR) OCT images within a short scanning time, we presented a learning‐based method to recover high‐quality OCT images from noisy and low‐resolution OCT images. We proposed a semi‐supervised learning approach named N2NSR‐OCT, to generate denoised and super‐resolved OCT images simultaneously using up‐ and down‐sampling networks ...

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    8. Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography

      Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography

      A multispectral fundus camera (MSFC), as a novel noninvasive technology, uses an extensive range of monochromatic light sources that enable the view of different sectional planes of the retinal and choroidal structures. However, MSFC imaging involves complex processes affected by various factors, and the recognized theory based on light absorption above the choroid is not sufficient. In an attempt to supplement the relevant explanations, in this study, we used optical coherence tomography (OCT), a three-dimensional tomography modality, to analyze MSFC results at the retina and choroid. The swept-source OCT system at 1060 nm wavelength with a 200 kHz A-scan rate ...

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    9. Comparative study of deep learning models for optical coherence tomography angiography

      Comparative study of deep learning models for optical coherence tomography angiography

      Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three ...

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    10. Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function

      Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function

      Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers’ eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with ...

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    1-10 of 10
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    Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function Comparative study of deep learning models for optical coherence tomography angiography Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semi‐supervised deep learning Weakly Supervised Deep Learning Based Optical Coherence Tomography Angiography Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images Automated Analysis of Choroidal Sublayer Morphologic Features in Myopic Children Using EDI-OCT by Deep Learning Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography Cascade Optical Coherence Tomography (C-OCT) for Surface Form Metrology of - ProQuest Comparison of Anterior Segment Measurements with a New Multifunctional Unit and Five Other Devices