1. Articles from Jean-Martial Mari

    1-5 of 5
    1. 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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    2. 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 (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 ...

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    3. 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 stain (i.e. highlight) 6 tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the Dice coefficient, sensitivity, and specificity. We further studied ...

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    4. 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 represent a tissue group. These four histograms formed a vector basis on which we ‘projected' each OCT volume in order to generate four digitally stained volumes P1 to P4. Digital ...

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    5. Optimization of coronary optical coherence tomography imaging using the attenuation-compensated technique: a validation study

      Optimization of coronary optical coherence tomography imaging using the attenuation-compensated technique: a validation study

      Purpose To optimize conventional coronary optical coherence tomography (OCT) images using the attenuation-compensated technique to improve identification of plaques and the external elastic lamina (EEL) contour. Method The attenuation-compensated technique was optimized via manipulating contrast exponent C, and compression exponent N, to achieve an optimal contrast and signal-to-noise ratio (SNR). This was applied to 60 human coronary lesions (38 native and 22 stented) ex vivo conventional coronary OCT images acquired from heart autopsies of 10 patients and matching histology was available as reference. Three independent reviewers assessed the conventional and attenuation-compensated OCT images blindly for plaque characteristics and EEL detection ...

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    1-5 of 5
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    1. (6 articles) National Institutes of Health
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    Optimization of coronary optical coherence tomography imaging using the attenuation-compensated technique: a validation study A Digital Staining Algorithm for 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 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 segment optic nerve head tissues in optical coherence tomography images The Effect of Anti-Tubercular Drugs on Retinal Nerve Fiber Layer Thickness Using Optical Coherence Tomography A Cross-sectional Optical Coherence Tomography Study in Patients on Taxane-based Therapy and A Case Report with the Literature Review Association of systemic inflammatory biomarkers with morphological characteristics of the coronary atherosclerotic plaque by intravascular optical coherence tomography. OCT angiography improves views of retina blood flow Real-time retinal layer segmentation of OCT Volumes with GPU and Deep learning Baylor College of Medicine Receives a 2020 NIH Grant for Biomechanics of Early Mammalian Cardiogenesis University of Houston Receives a 2020 NIH Grant for Biomechanics of Neural Tube Development using Brillouin-OCT Multimodality