1. Articles from Xiangxi Meng

    1-5 of 5
    1. 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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    2. 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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    3. 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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    4. 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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    5. 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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    1-5 of 5
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  2. Topics in the News

    1. (4 articles) Peking University
    2. (4 articles) Qiushi Ren
    3. (3 articles) University of Erlangen
    4. (3 articles) Gangjun Liu
    5. (1 articles) Chuanqing Zhou
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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 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 Customized Slab-Segmentation Method for Projection-Artifact Elimination in Best Vitelliform Macular Dystrophy: A Swept-Source Optical Coherence Tomography Angiography Study Corneal Epithelial Thickness Profile in Healthy Portuguese Children by High-Definition Optical Coherence Tomography Prediction model for best focus, power, and spherical aberration of the cornea: Raytracing on a large dataset of OCT data Classification of pachychoroid on optical coherence tomography using deep learning Noninvasive Evaluation of HepaRG Aggregates during Drug‐Induced Intrahepatic Cholestasis Using Optical Coherence Tomography Online measurement of floc size, viscosity, and consistency of cellulose microfibril suspensions with optical coherence tomography A Joint Model for Macular Edema Analysis in Optical Coherence Tomography Images Based on Image Enhancement and Segmentation