1. Articles from SHENGHUA GAO

    1-4 of 4
    1. BIONET: INFUSING BIOMARKER PRIOR INTO GLOBAL-TO-LOCAL NETWORK FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      BIONET: INFUSING BIOMARKER PRIOR INTO GLOBAL-TO-LOCAL NETWORK FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES

      Choroid is the vascular layer of the eye, which is directly related to the incidence and severity of many ocular diseases. Optical Coherence Tomography (OCT) is capable of imaging both the cross-sectional view of retina and choroid, but the segmentation of the choroid region is challenging because of the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker infused global-to-local network (BioNet) for choroid segmentation, which segments the choroid with higher credibility and robustness. Firstly, our method trains a biomarker prediction network to learn the features of the biomarker. Then a global multi-layers segmentation module is applied to ...

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    2. 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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    3. 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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    4. Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

      Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

      Optical coherence tomography angiography (OCTA) requires high transverse sampling rates for visualizing retinal and choroidal capillaries, which impedes the popularization of the OCTA technique due to the high cost of speedy acquisition systems. On the other hand, current wide-field OCTA using low transverse sampling causes the underestimation of vascular biomarkers in quantitative analysis. In this paper, we propose to use deep learning to repair the resolution degeneration induced by the low transverse sampling. We conducted preliminary experiments on converting the centrally cropped 3 × 3 mm2 field of view (FOV) of the 8 × 8 mm2 foveal OCTA images (a sampling rate ...

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    1-4 of 4
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    1. (6 articles) National Institutes of Health
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    Resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning 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 BIONET: INFUSING BIOMARKER PRIOR INTO GLOBAL-TO-LOCAL NETWORK FOR CHOROID SEGMENTATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES The Effect of Anti-Tubercular Drugs on Retinal Nerve Fiber Layer Thickness Using Optical Coherence Tomography A Cross-sectional Optical Coherence Tomography Study in Patients on Taxane-based Therapy and A Case Report with the Literature Review Association of systemic inflammatory biomarkers with morphological characteristics of the coronary atherosclerotic plaque by intravascular optical coherence tomography. OCT angiography improves views of retina blood flow Real-time retinal layer segmentation of OCT Volumes with GPU and Deep learning Baylor College of Medicine Receives a 2020 NIH Grant for Biomechanics of Early Mammalian Cardiogenesis University of Houston Receives a 2020 NIH Grant for Biomechanics of Neural Tube Development using Brillouin-OCT Multimodality Oregon Health and Sciences University Receives a 2020 NIH Grant  for Artificial Intelligence Assisted Panoramic Optical Coherence Tomography for Retinopathy of Prematurity