1. Articles from Giridhar Subramanian

    1-4 of 4
    1. Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning

      Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning

      Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device-specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, the complexity in deploying DL algorithms clinically is only increasing. To address this, we propose a DLbased 3D ...

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    2. A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

      A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

      Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 ‘clean B-scans’ (multi-frame B-scans; signal averaged), and their corresponding ‘noisy B-scans’ (clean B-scans + Gaussian noise), we were able to ...

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    3. A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

      A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

      Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising ...

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    4. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images

      DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images

      Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer ...

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    1-4 of 4
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  2. Topics in the News

    1. (3 articles) National University of Singapore
    2. (3 articles) Singapore Eye Research Institute
    3. (2 articles) Medical University of Vienna
    4. (2 articles) Nanyang Technological University
    5. (1 articles) University of Sydney
    6. (1 articles) Duke University
    7. (1 articles) University College London
    8. (1 articles) Columbia University
    9. (1 articles) University of Rochester
    10. (1 articles) Stanford University
    11. (1 articles) Johns Hopkins University
    12. (1 articles) National University of Singapore
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