1. Articles from Tan Hung Pham

    1-6 of 6
    1. Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

      Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

      Background/Aims Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. Method In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important ...

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    2. 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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    3. DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

      DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

      Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: 2328 OCT images acquired through the center of the ONH using a Spectralis OCT machine for both eyes of 13 subjects were used to train a generative adversarial network (GAN) using a custom loss function.Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast € a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images. Œis was computed in the Retinal Nerve Fiber Layer (RNFL), the Inner Plexiform Layer (IPL), the ...

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    4. 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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    5. Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images

      Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images

      Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle closure glaucoma. In this study, we developed a deep convolutional neural network (DCNN) for the localization of the scleral spur, and the segmentation of anterior segment structures (iris, corneo-sclera shell, anterior chamber). With limited training data, the DCNN was able to detect the scleral spur on unseen ASOCT images as accurately as an experienced ophthalmologist; and simultaneously isolated the anterior segment structures with a Dice coefficient ...

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    6. 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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    1-6 of 6
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  2. Topics in the News

    1. (5 articles) National University of Singapore
    2. (4 articles) Singapore Eye Research Institute
    3. (3 articles) Nanyang Technological University
    4. (2 articles) Medical University of Vienna
    5. (2 articles) Cardiff University
    6. (1 articles) Duke University
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    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 DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images Cascade Optical Coherence Tomography (C-OCT) for Surface Form Metrology of - ProQuest Comparison of Anterior Segment Measurements with a New Multifunctional Unit and Five Other Devices Efficacy of Notal Vision Home OCT demonstrated by a series of scientific and clinical work Synergy Between morpHOlogical and inflammatoRy Evaluation in Predicting Long-term Coronary Plaque Progression Altered ocular microvasculature in patients with systemic sclerosis and very early disease of systemic sclerosis using optical coherence tomography angiography Assessment of macular findings by OCT angiography in patients without clinical signs of diabetic retinopathy: radiomics features for early screening of diabetic retinopathy