1. Articles from Shamira Perera

    1-15 of 15
    1. Towards ‘automated gonioscopy’: a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography

      Towards ‘automated gonioscopy’: a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography

      Aims: To validate a deep learning (DL) algorithm (DLA) for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan). Methods: This was a reliability analysis from a cross-sectional study. An independent test set of 39 936 SS-OCT scans from 312 phakic subjects (128 SS-OCT meridional scans per eye) was analysed. Participants above 50 years with no previous history of intraocular surgery were consecutively recruited from glaucoma clinics. Indentation gonioscopy and dark room SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle. For each ...

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    2. Evaluation of meridional scans for angle closure assessment with anterior segment swept-source optical coherence tomography

      Evaluation of meridional scans for angle closure assessment with anterior segment swept-source optical coherence tomography

      Background/aims As swept-source optical coherence tomography (SS-OCT) simultaneously obtains 128 meridional scans, it is important to identify which scans are playing the main role in classifying gonioscopic angle closure to simplify the analysis. We aimed to evaluate the diagnostic performance of every meridional scan in its ability to detect gonioscopic angle closure. Methods Observational study with 2027 phakic subjects consecutively recruited from a community polyclinic. Gonioscopy and SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle, while SS-OCT was defined as iridotrabecular contact anterior to the scleral spur ...

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    3. 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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    4. 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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    5. 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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    6. Understanding diagnostic disagreement in angle closure assessment between anterior segment optical coherence tomography and gonioscopy

      Understanding diagnostic disagreement in angle closure assessment between anterior segment optical coherence tomography and gonioscopy

      Background/aims Although being a more objective tool for assessment and follow-up of angle closure, reliability studies have reported a moderate diagnostic performance for anterior segment optical coherence tomography (OCT) technologies when comparing with gonioscopy as the reference standard. We aim to determine factors associated with diagnostic disagreement in angle closure when assessed by anterior segment swept source OCT (SS-OCT, CASIA SS-1000; Tomey, Nagoya, Japan) and gonioscopy. Methods Cross-sectional study. A total of 2027 phakic subjects aged ≥50 years, with no relevant previous ophthalmic history, were consecutively recruited from a community polyclinic in Singapore. Gonioscopy and SS-OCT (128 radial scans ...

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    7. 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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    8. 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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    9. 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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    10. 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

      Glaucoma is characterized by an irreversible damage of retinal ganglion cells within the optic nerve head (ONH) at the back of the eye. Currently we know that elevated intraocular pressure (IOP) is associated with increased prevalence of glaucoma but not all glaucoma patients have an elevated IOP. The biomechanical theory of glaucoma hypothesizes that elevated (or fluctuating) IOP deforms the ONH tissues, including the lamina cribrosa (LC), and that these deformations drive retinal ganglion cell injury and death. However, IOP is not the only load that can deform the ONH. Eye movements have recently been hypothesized to be able to ...

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    11. 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), in different gaze positions (adduction and abduction of 20°) and following an acute IOP elevation of approximately 20 mm Hg from baseline (via ophthalmodynamometry). Strains of LC for all loading ...

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    12. Methods And Systems For Characterizing Angle Closure Glaucoma For Risk Assessment Or Screening

      Methods And Systems For Characterizing Angle Closure Glaucoma For Risk Assessment Or Screening

      A method is proposed for analysing an optical coherence tomography (OCT) image of the anterior segment (AS) of a subject's eye. A region of interest is defined which is a region of the image containing the junction of the cornea and iris, and an estimated position the junction within the region of interest is derived. Using this a second region of the image is obtained, which is a part of the image containing the estimated position of the junction. Features of the second region are obtained, and those features are input to an adaptive model to generate data characterizing ...

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    13. Automated anterior chamber angle localization and glaucoma type classification in OCT images

      Automated anterior chamber angle localization and glaucoma type classification in OCT images

      To identify glaucoma type with OCT (optical coherence tomography) images, we present an image processing and machine learning based framework to localize and classify anterior chamber angle (ACA) accurately and efficiently. In digital OCT photographs, our method automatically localizes the ACA region, which is the primary structural image cue for clinically identifying glaucoma type. Next, visual features are extracted from this region to classify the angle as open angle (OA) or angle-closure (AC). This proposed method has three major contributions that differ from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA ...

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    14. Imaging late capsular bag distension syndrome: an anterior segment optical coherence tomography study

      Imaging late capsular bag distension syndrome: an anterior segment optical coherence tomography study

      Background: Anterior segment optical coherence tomography (ASOCT) was used to categorize and provide insights into the etiology of capsular bag distension syndrome (CBDS). Methods: A prospective review was undertaken of 10 cases who presented with signs of late CBDS 5–11 years after uneventful phacoemulsification with in-the-bag posterior chamber intraocular lens implantation. Results: All 10 patients presented with a milky collection within the distended capsular bag without raised intraocular pressure or a shallow anterior chamber. ASOCT was used to confirm the diagnosis in all cases, and a hyperintense signal was seen in the space between the posterior chamber intraocular lens ...

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    15. Anterior segment imaging in the management of postoperative fibrin pupillary–block glaucoma

      Postoperative fibrin pupillary–block glaucoma, an uncommon complication of intraocular surgery, develops when an inflammatory fibrin membrane occludes the pupil, resulting in peripheral angle closure. We present a series of 4 patients with this condition and describe the role of anterior segment optical coherence tomography and ultrasound biomicroscopy in distinguishing fibrin pupillary–block glaucoma from other forms of postoperative acute glaucoma. Specific to this condition is the presence of a fibrin membrane across the pupil and accumulation of aqueous in the posterior chamber, as would be expected in pupil block, but with a clear separation between the intraocular lens and ...
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    1-15 of 15
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  2. Topics in the News

    1. (10 articles) National University of Singapore
    2. (10 articles) Singapore Eye Research Institute
    3. (4 articles) Nanyang Technological University
    4. (3 articles) University of Sydney
    5. (3 articles) Medical University of Vienna
    6. (3 articles) University College London
    7. (2 articles) Duke University
    8. (2 articles) Tomey Corporation
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    Imaging late capsular bag distension syndrome: an anterior segment optical coherence tomography study Automated anterior chamber angle localization and glaucoma type classification in OCT images Methods And Systems For Characterizing Angle Closure Glaucoma For Risk Assessment Or Screening 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 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 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 Optical coherence tomography findings in patients with transfusion-dependent β-thalassemia Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea Optical coherence tomography image based eye disease detection using deep convolutional neural network