1. Articles from Yuhui Ma

    1-4 of 4
    1. PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY

      PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY

      Accurate choroid segmentation in optical coherence tomography (OCT) image is vital because the choroid thickness is a major quantitative biomarker of many ocular diseases. Deep learning has shown its superiority in the segmentation of the choroid region but subjects to the performance degeneration caused by the domain discrepancies (e.g., noise level and distribution) among datasets obtained from the OCT devices of different manufacturers. In this paper, we present an unsupervised perceptual-assisted adversarial adaptation (PAAA) framework for efficiently segmenting the choroid area by narrowing the domain discrepancies between different domains. The adversarial adaptation module in the proposed framework encourages the ...

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    2. High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

      High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

      Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to-noise ratio (SNR) of OCT images. In this paper, we propose to use deep learning for the reconstruction of the high SNR OCT images from the low bit-depth acquisition. Its feasibility was preliminarily evaluated by applying the proposed method to the quantized 3 ∼ 8-bit data from native 12-bit interference fringes. We employed a pixel-to-pixel generative adversarial network ...

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    3. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level. The proposed deep convolutional neural network (CNN) applies strategies including residual learning, shortcut connection, batch normalization and leaky rectified linear units to achieve good despeckling performance. Application of the proposed method to the OCT images shows great improvement in both visual quality and quantitative indices. The proposed method provides ...

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

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

      Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance ...

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    1-4 of 4
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    1. (2 articles) Soochow University
    2. (1 articles) Fudan University
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    Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning PERCEPTUAL-ASSISTED ADVERSARIAL ADAPTATION FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY Optical coherence tomography angiography (OCTA) findings in Serpiginous Choroiditis Changes in retinal layer thickness with maturation in the dog: an in vivo spectral domain - optical coherence tomography imaging study Optical coherence tomography angiography analysis of fabry disease An unsupervised hierarchical approach for automatic intra‐retinal cyst segmentation in spectral‐domain optical coherence tomography images Towards quantitative assessment of burn based on photoacoustic and optical coherence tomography Cellular Scale Imaging of Transparent Retinal Structures and Processes Using Adaptive Optics Optical Coherence Tomography Optical Coherence Tomography Imaging in Acute Myocardial Infarction: Calcified Nodule as a Culprit Lesion Study of low-peak-power highly coherent broadband supercontinuum generation through a dispersion-engineered Si-rich silicon nitride waveguide