1. Articles from Bhavna Antony

    1-6 of 6
    1. Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

      Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images

      The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore ...

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    2. 3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

      3D-CNN for Glaucoma Detection Using Optical Coherence Tomography

      The large size of raw 3D optical coherence tomography (OCT) volumes poses challenges for deep learning methods as it cannot be accommodated on a single GPU in its original resolution. The direct analysis of these volumes however, provides advantages such as circumventing the need for the segmentation of retinal structures. Previously, a deep learning (DL) approach was proposed for the detection of glaucoma directly from 3D OCT volumes, where the volumes were significantly downsampled first. In this paper, we propose an end-to-end DL model for the detection of glaucoma that doubles the number of input voxels of the previously proposed ...

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    3. Inference of visual field test performance from OCT volumes using deep learning

      Inference of visual field test performance from OCT volumes using deep learning

      Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression. Here we address the question to what degree aggregate VFT measurements such as Visual Field Index (VFI) and Mean Deviation (MD) can be inferred from Optical Coherence Tomography (OCT) scans of the Optic Nerve Head (ONH) or the macula. Accurate inference of VFT measurements from OCT could reduce examination time and cost. We propose a novel 3D Convolutional Neural Network (CNN) for this task and compare its accuracy with classical machine learning (ML) algorithms trained on common, segmentation-based OCT, features employed for glaucoma diagnostics ...

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    4. A feature agnostic approach for glaucoma detection in OCT volumes

      A feature agnostic approach for glaucoma detection in OCT volumes

      Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based ...

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    5. Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

      Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

      Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art ...

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    6. Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images

      Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images

      The 3-D spectral-domain optical coherence tomography (SD-OCT) images of the retina often do not reflect the true shape of the retina and are distorted differently along the x and y axes. In this paper, we propose a novel technique that uses thin-plate splines in two stages to estimate and correct the distinct axial artifacts in SD-OCT images. The method was quantitatively validated using nine pairs of OCT scans obtained with orthogonal fast-scanning axes, where a segmented surface was compared after both datasets had been corrected. The mean unsigned difference computed between the locations of this artifact-corrected surface after the single-spline ...

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    1-6 of 6
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    1. (4 articles) NYU Langone Medical Center
    2. (4 articles) Gadi Wollstein
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    Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning A feature agnostic approach for glaucoma detection in OCT volumes Inference of visual field test performance from OCT volumes using deep learning 3D-CNN for Glaucoma Detection Using Optical Coherence Tomography Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images Non-invasive diagnosis of acquired lymphangiectases using optical coherence tomography Depth-resolved investigation of multiple optical properties and wrinkle morphology in eye-corner areas with multi-contrast Jones matrix optical coherence tomography Lipemia Retinalis: Hyperreflective Vascular Changes, Detected by Optical Coherence Tomography Enhanced Depth Imaging Optical Coherence Tomography Technology Reveals a Significant Association Between Optic Nerve Drusen Anterior Displacement and Retinal Nerve Fiber Layer Thinning Over Time Comparison of Peripapillary Vessel Density of Acute Nonarteritic Anterior Ischemic Optic Neuropathy and Other Optic Neuropathies With Disc Swelling Using Optical Coherence Tomography Angiography: A Pilot Study Optical Coherence Tomography Angiography Characteristics and Predictors of Visual Outcomes in Patients with Acute and Chronic Nonarteritic Anterior Ischemic Optic Neuropathy