1. Michaël J. A. Girard

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    1. Mentioned In 34 Articles

    2. 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 ...
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    3. Longitudinal assessment of optic nerve head changes using optical coherence tomography in a primate microbead model of ocular hypertension

      Longitudinal assessment of optic nerve head changes using optical coherence tomography in a primate microbead model of ocular hypertension
      In humans, the longitudinal characterisation of early optic nerve head (ONH) damage in ocular hypertension (OHT) is difficult as patients with glaucoma usually have structural ONH damage at the time of diagnosis. Previous studies assessed glaucomatous ONH cupping by measuring the anterior lamina cribrosa depth (LCD) and minimal rim width (MRW) using optical coherence tomography (OCT). In this study, we induced OHT by repeated intracameral microbead injections in 16 cynomolgus ...
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    4. OCT Optic Nerve Head Morphology in Myopia I: Implications of Anterior Scleral Canal Opening versus Bruch’s Membrane Opening Offset

      OCT Optic Nerve Head Morphology in Myopia I: Implications of Anterior Scleral Canal Opening versus Bruch’s Membrane Opening Offset
      Purpose To measure the magnitude and direction of anterior scleral canal opening (ASCO) offset relative to Bruchs membrane opening (BMO) (ASCO/BMO offset) in order to characterize neural canal obliqueness and minimum cross-sectional area (NCMCA) in 69 highly myopic and 138 healthy, age-matched, control eyes. Design: Cross-sectional study. Methods Using Optic Coherence Tomography (OCT) scans of the optic nerve head (ONH), BMO and ASCO were manually segmented and their centroids ...
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    5. 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 ...
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    6. Efficacy and Reproducibility of Attenuation-Compensated Optical Coherence Tomography for Assessing External Elastic Membrane Border and Plaque Composition in Native and Stented Segments ― An In Vivo and Histology-Based Study ―

      Efficacy and Reproducibility of Attenuation-Compensated Optical Coherence Tomography for Assessing External Elastic Membrane Border and Plaque Composition in Native and Stented Segments ― An In Vivo and Histology-Based Study ―
      Background: Attenuation-compensated (AC) technique was recently introduced to improve the plaque characterization of optical coherence tomography (OCT). Histological validation demonstrated promising results but the efficacy and reproducibility of this technique for assessing in-vivo tissue composition remains unclear. Methods and Results: OCT images portraying native (n=200) and stented (n=200) segments and 31 histological cross-sections were analyzed. AC-OCT appeared superior to conventional (C)-OCT in detecting the external elastic lamina ...
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    7. 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 ...
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    8. 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 ...
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    9. 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 ...
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    10. 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 ...
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    11. DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images

      DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain 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 was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local ...
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    12. Imaging of the lamina cribrosa and its role in glaucoma: a review

      Imaging of the lamina cribrosa and its role in glaucoma: a review
      The lamina cribrosa of the optic nerve head serves two contrasting roles; it must be porous to allow retinal ganglion cell axons to pass through, and yet at the same time, it must also provide adequate structural support to withstand the stresses and strains across it. Improvements in imaging such as optical coherence tomography image capture and image processing have allowed detailed in vivo studies of lamina cribrosa macro- and ...
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    13. A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head

      A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head
      Purpose. To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods. A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for 1 eye of each of 100 subjects (40 normal 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally ...
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    14. Feature Of The Week 05/28/2017: In Vivo 3-Dimensional Strain Mapping Confirms Large Optic Nerve Head Deformations Following Horizontal Eye Movements

      Feature Of The Week 05/28/2017: In Vivo 3-Dimensional Strain Mapping Confirms Large Optic Nerve Head Deformations Following Horizontal Eye Movements
      ...nts and axonal loss in optic neuropathies. For more information see recent Article. Courtesy Xiaofei Wang and Michaël J. A. Girard from National University of Singapore....
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    15. A Digital Staining Algorithm for Optical Coherence Tomography Images of the Optic Nerve Head

      A Digital Staining Algorithm for Optical Coherence Tomography Images of the Optic Nerve Head
      Purpose : To digitally stain spectral-domain optical coherence tomography (OCT) images of the optic nerve head (ONH), and highlight either connective or neural tissues. Methods : OCT volumes of the ONH were acquired from one eye of 10 healthy subjects. We processed all volumes with adaptive compensation to remove shadows and enhance deep tissue visibility. For each ONH, we identified the four most dissimilar pixel-intensity histograms, each of which was assumed to ...
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    16. In Vivo 3-Dimensional Strain Mapping Confirms Large Optic Nerve Head Deformations Following Horizontal Eye Movements

      In Vivo 3-Dimensional Strain Mapping Confirms Large Optic Nerve Head Deformations Following Horizontal Eye Movements
      Purpose : To measure lamina cribrosa (LC) strains (deformations) following abduction and adduction in healthy subjects and to compare them with those resulting from a relatively high acute intraocular pressure (IOP) elevation. Methods : A total of 16 eyes from 8 healthy subjects were included. Among the 16 eyes, 11 had peripapillary atrophy (PPA). For each subject, both optic nerve heads (ONHs) were imaged using optical coherence tomography (OCT) at baseline (twice ...
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  2. About Michaël J. A. Girard

    Michaël J. A. Girard

    Michael Girard is an Assistant Professor in the Department of Bioengineering at the National University of Singapore (see lab website Here). Dr Girard received his engineering diploma (equivalent to a MSc) in Mechanical Engineering from the Ecole Polytechnique Universitaire de Lyon, France in 2003. He was awarded his PhD from the Department of Biomedical Engineering at Tulane University, New Orleans, USA, in January of 2009. Subsequently, Dr Girard pursued his postdoctoral work in the Department of Bioengineering at Imperial College London, UK. In September of 2010, Dr Girard was awarded a highly competitive and prestigious Imperial College Junior Research Fellowship to pursue his research. Throughout his training, Dr Girard has gained expertise in experimental, theoretical, and computational soft tissue biomechanics, which he has utilised to pose and answer questions of high clinical relevance in the field of ophthalmology. Dr Girard's current research program aims to develop state-of-the-art engineering tools for the diagnosis and treatment of biomechanically-related pathologies.