1. Articles from YAN HU

    1-4 of 4
    1. Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation

      Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation

      Optical coherence tomography (OCT) is a rapidly developing non-invasive three dimensional imaging approach, and it has been widely used in examination and diagnosis of eye diseases. However, speckle noise are often inherited from image acquisition process, and may obscure the anatomical structure, such as the retinal layers. In this paper, we propose a novel method to reduce the speckle noise in 3D OCT scans, by introducing a new super-resolution approach. It uses a multi-frame fusion mechanism that merges multiple scans for the same scene, and utilizes the movements of sub-pixels to recover missing signals in one pixel, which significantly improves ...

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    2. Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning

      Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning

      The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo, because it does not suffer from the information contamination of the outer retina in fundus photography and scanning laser ophthalmoscopy and the resolution deficiency in ocular ultrasound. We propose a biomarker infused global-to-local network, for the choroid segmentation. It leverages the thickness of the choroid layer, which is a primary biomarker in clinic, as a constraint to improve the segmentation accuracy. We also design a global-to-local ...

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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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  2. Topics in the News

    1. (6 articles) National Institutes of Health
    2. (2 articles) Chinese Academy of Sciences
    3. (2 articles) UC Irvine
    4. (2 articles) Oregon Health & Science University
    5. (2 articles) Zhongping Chen
    6. (2 articles) Yifan Jian
    7. (2 articles) Heidelberg Engineering
    8. (1 articles) Sun Yat-Sen University
    9. (1 articles) University of Houston
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    11. (1 articles) Kirill V. Larin
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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 Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation 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