1. Articles from José Ignacio Orlando

    1-5 of 5
    1. Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning

      Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning

      Purpose: Currently used measures of retinal function are limited by being subjective, non-localized and/or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict a functional endpoint (retinal sensitivity) from structural optical coherence tomography (OCT) images. Design: Retrospective cross-sectional study. Subjects: In total, 714 volumes of 289 patients were used in this study. Methods: A novel deep learning algorithm was developed to automatically predict a comprehensive retinal sensitivity map from OCTs. 463 SD-OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development ...

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    2. AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a ...

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    3. AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

      Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually leads to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a ...

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    4. Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

      Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

      Diabetic macular edema (DME) and retina vein occlusion (RVO) are macular diseases in which central photoreceptors are affected due to pathological accumulation of fluid. Optical coherence tomography allows to visually assess and evaluate photoreceptor integrity, whose alteration has been observed as an important biomarker of both diseases. However, the manual quantification of this layered structure is challenging, tedious and time-consuming. In this paper we introduce a deep learning approach for automatically segmenting and characterising photoreceptor alteration. The photoreceptor layer is segmented using an ensemble of four different convolutional neural networks. En-face representations of the layer thickness are produced to characterize ...

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    5. Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography

      Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography

      Automated drusen segmentation in retinal optical coherence tomography (OCT) scans is relevant for understanding age-related macular degeneration (AMD) risk and progression. This task is usually performed by segmenting the top/bottom anatomical interfaces that define drusen, the outer boundary of the retinal pigment epithelium (OBRPE) and the Bruch's membrane (BM), respectively. In this paper we propose a novel multi-decoder architecture that tackles drusen segmentation as a multitask problem. Instead of training a multiclass model for OBRPE/BM segmentation, we use one decoder per target class and an extra one aiming for the area between the layers. We also introduce ...

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    1-5 of 5
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    1. (5 articles) Medical University of Vienna
    2. (2 articles) Sun Yat-Sen University
    3. (2 articles) Soochow University
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    5. (1 articles) Shanghai Jiao Tong University
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