1. Articles from Joel Schuman

    1-10 of 10
    1. Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography

      Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography

      Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology. However, OCT images are strongly corrupted by noise, limiting their interpretation. Current OCT denoisers leverage assumptions on noise distributions or generate targets for training deep supervised denoisers via averaging of repeat acquisitions. However, recent self-supervised advances allow the training of deep denoising networks using only repeat acquisitions without clean targets as ground truth, reducing the burden of supervised learning. Despite the clear advantages of self-supervised methods, their use is precluded as OCT shows strong structural deformations even between sequential scans of the same subject due ...

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    2. 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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    3. Improved Denoising of Optical Coherence Tomography via Repeated Acquisitions and Unsupervised Deep Learning

      Improved Denoising of Optical Coherence Tomography via Repeated Acquisitions and Unsupervised Deep Learning

      Purpose : Optical Coherence Tomography (OCT) is widely used, yet its interpretation is confounded by strong speckle noise. Previous denoising methods make strong assumptions on noise characteristics and struggle to retain fine structure. We present a data-driven registration and denoising method which vastly improves image fidelity while requiring only repeated noisy acquisitions of individual subjects. Methods : Noise2Noise is an unsupervised denoising approach for repeatedly acquired noisy images (Lehtinen, ICML , 2018). By training a convolutional neural network on noisy image pairs with zero-mean noise, the mean of the clean image distribution is learned. However, repeats are assumed to vary only in noise ...

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    4. In Vivo Imaging of Schlemm's Canal and Limbal Vascular Network in Mouse Using Visible-Light OCT

      In Vivo Imaging of Schlemm's Canal and Limbal Vascular Network in Mouse Using Visible-Light OCT

      Purpose : To validate the ability of visible-light optical coherence tomography (vis-OCT) in imaging the full Schlemm's canal (SC) and its surrounding limbal vascular network in mice in vivo through a compound circumlimbal scan. Methods : We developed an anterior segment vis-OCT system and a compound circumlimbal scanning method, which montages eight rotated raster scans. We calibrated the circumlimbal scan geometry using a three-dimensional printed phantom eyeball before imaging wild-type C57BL/6J mice. We measured SC size by segmenting SC cross sections from vis-OCT B-scan images and imaged the limbal microvascular network using vis-OCT angiography (vis-OCTA). To introduce changes in SC ...

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    5. Macroaneurysms Associated With Congenital Retinal Macrovessels

      Macroaneurysms Associated With Congenital Retinal Macrovessels

      Purpose: Congenital retinal macrovessels are large aberrant retinal blood vessels that cross the horizontal raphe and can traverse the central macula. Using multimodal imaging and optical coherence tomography angiography, we describe 2 cases of congenital retinal macrovessel associated with macroaneurysms. Methods: Two patients presented for evaluation and were found to have congenital retinal macrovessels associated with macroaneurysms. Color photography, optical coherence tomography, fundus autofluorescence fluorescein angiography, and optical coherence tomography angiography were performed and used to establish the diagnosis and monitor resolution at follow-up visits. Results: The first patient presented with central vision loss in the right eye and was ...

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    6. 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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    7. 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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    8. Correcting Motion Artifacts in Retinal Spectral Domain Optical Coherence Tomography via Image Registration

      Spectral domain optical coherence tomography (SD-OCT) is an important tool for the diagnosis of various retinal diseases. The measurements available from SD-OCT volumes can be used to detect structural changes in glaucoma patients before the resulting vision loss becomes noticeable. Eye movement during the imaging process corrupts the data, making measurements unreliable. We propose a method to correct for transverse motion artifacts in SD-OCT volumes after scan acquisition by registering the volume to an instantaneous, and therefore artifact-free, reference image. Our procedure corrects for smooth deformations resulting from ocular tremor and drift as well as the abrupt discontinuities in vessels ...
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    9. Three-dimensional ultrahigh resolution optical coherence tomography imaging of age-related macular degeneration

      Three-dimensional ultrahigh resolution optical coherence tomography imaging of age-related macular degeneration

      Ultrahigh resolution optical coherence tomography (OCT) enhances the ability to visualize different intra retinal layers. In age-related macular degeneration (AMD), pathological changes in individual retinal layers, including photoreceptor inner and outer segments and retinal pigment ... [Opt. Express 17, 4046-4060 (2009)]

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    10. Glaucoma diagnosis by mapping macula with Fourier domain optical coherence tomography

      Ou Tan, Ake Lu, Vik Chopra et al. A new image segmentation method was developed to detect macular retinal sub-layers boundary on newly-developed Fourier-Domain Optical Coherence Tomography (FD-OCT) with macular grid scan pattern. The segmentation results were used to create thickness map of macular ganglion cell complex (GCC), which ... [Proc. SPIE Int. Soc. Opt. Eng. 6915, 69153L (2008)] published Mon Mar 17, 2008.
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    1-10 of 10
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    1. (7 articles) NYU Grossman School of Medicine
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    Three-dimensional ultrahigh resolution optical coherence tomography imaging of age-related macular degeneration A feature agnostic approach for glaucoma detection in OCT volumes 3D-CNN for Glaucoma Detection Using Optical Coherence Tomography Macroaneurysms Associated With Congenital Retinal Macrovessels In Vivo Imaging of Schlemm's Canal and Limbal Vascular Network in Mouse Using Visible-Light OCT Improved Denoising of Optical Coherence Tomography via Repeated Acquisitions and Unsupervised Deep Learning Attention-guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association using Volumetric Images Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography Editorial – Optical Coherence Tomography Angiography: Considerations Regarding Diagnostic Parameters Imaging of the optic nerve: technological advances and future prospects Optical coherence tomography assessment of pulmonary vascular remodeling in advanced heart failure. The OCTOPUS-CHF study Systems and methods for automated widefield optical coherence tomography angiography