1. Articles from Hrvoje Bogunovic

    1-20 of 20
    1. Identification and quantification of fibrotic areas in the human retina using polarization-sensitive OCT

      Identification and quantification of fibrotic areas in the human retina using polarization-sensitive OCT

      Subretinal fibrosis is one of the most prevalent causes of blindness in the elderly population, but a true gold standard to objectively diagnose fibrosis is still lacking. Since fibrotic tissue is birefringent, it can be detected by polarization-sensitive optical coherence tomography (PS-OCT). We present a new algorithm to automatically detect, segment, and quantify fibrotic lesions within 3D data sets recorded by PS-OCT. The algorithm first compensates for the birefringence of anterior ocular tissues and then uses the uniformity of the birefringent optic axis as an indicator to identify fibrotic tissue, which is then segmented and quantified. The algorithm was applied ...

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    2. Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration

      Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration

      Objectives To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration. Methods This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit. Hyperreflective foci (HRF) were quantitatively assessed in SD-OCT volumes using a validated deep learning algorithm. FAF images were graded for FAF patterns, subretinal drusenoid deposits (SDD), GA lesion configuration and atrophy enlargement. Linear mixed models were calculated to investigate associations between all parameters and GA ...

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    3. Deep Learning–Based Automated Optical Coherence Tomography Segmentation in Clinical Routine: Getting Closer

      Deep Learning–Based Automated Optical Coherence Tomography Segmentation in Clinical Routine: Getting Closer

      Recently, many ophthalmologists have heard the keywords artificial intelligence , machine learning , deep learning , and automatization at every conference and keynote lecture and seen them in every ophthalmology journal. 1 Many studies 1 have evaluated the use of such algorithms on large retrospective data sets—primarily on color fundus photographs at first, then on optical coherence tomography (OCT) images as well. Most of these have been study data sets with standardized and well-structured imaging protocols and reading center image collections with a predefined protocol, and therefore of good quality. However, how functional will algorithms be in a busy clinical routine? Can ...

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    4. Spatio-temporal alterations in retinal and choroidal layers in the progression of age-related macular degeneration (AMD) in optical coherence tomography

      Spatio-temporal alterations in retinal and choroidal layers in the progression of age-related macular degeneration (AMD) in optical coherence tomography

      Age-related macular degeneration (AMD) is the predominant cause of vision loss in the elderly with a major impact on ageing societies and healthcare systems. A major challenge in AMD management is the difficulty to determine the disease stage, the highly variable progression speed and the risk of conversion to advanced AMD, where irreversible functional loss occurs. In this study we developed an optical coherence tomography (OCT) imaging based spatio-temporal reference frame to characterize the morphologic progression of intermediate age-related macular degeneration (AMD) and to identify distinctive patterns of conversion to the advanced stages macular neovascularization (MNV) and macular atrophy (MA ...

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    5. 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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    6. Predicting Progression of Age-Related Macular Degeneration Using Optical Coherence Tomography and Fundus Photography

      Predicting Progression of Age-Related Macular Degeneration Using Optical Coherence Tomography and Fundus Photography

      Purpose To compare the performance of automatically quantified optical coherence tomography (OCT) imaging biomarkers and conventional risk factors manually graded on color fundus photographs (CFP) for predicting progression to late age-related macular degeneration (AMD). Design Longitudinal observational study. Participants 280 eyes from 140 participants with bilateral large drusen. Methods All participants underwent OCT and CFP imaging at baseline and were then reviewed at six-monthly intervals to determine progression to late AMD. CFPs were manually graded and OCT scans underwent automated image analyses to quantify risk factors and imaging biomarkers respectively based on drusen and AMD pigmentary abnormalities. Four predictive models ...

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    7. End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

      End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

      Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but interindividual treatment requirements are vastly heterogeneous and currently poorly plannable resulting in suboptimal treatment frequency. Optical coherence tomography (OCT) with its 3D high-resolution imaging serves as a companion diagnostic to anti-VEGF therapy. This creates a need for building predictive models using automated image analysis of OCT scans acquired during the treatment initiation phase. We propose such a model based on deep learning (DL) architecture, comprised of a densely connected neural network (DenseNet) and a recurrent neural network (RNN), trainable end-to-end. The method starts by sampling several 2D-images ...

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    8. 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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    9. Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography

      Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography

      Importance The morphologic changes and their pathognomonic distribution in progressing age-related macular degeneration (AMD) are not well understood. Objectives To characterize the pathognomonic distribution and time course of morphologic patterns in AMD and to quantify changes distinctive for progression to macular neovascularization (MNV) and macular atrophy (MA). Design, Setting, and Participants This cohort study included optical coherence tomography (OCT) volumes from study participants with early or intermediate AMD in the fellow eye in the HARBOR (A Study of Ranibizumab Administered Monthly or on an As-needed Basis in Patients With Subfoveal Neovascular Age-Related Macular Degeneration) trial. Patients underwent imaging monthly for ...

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    10. 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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    11. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration

      Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration

      Purpose Anti-VEGF treatment of neovascular age-related macular degeneration (AMD) is a highly effective advance in the retinal armentarium. Optical coherence tomography (OCT) offering three-dimensional imaging of the retina is widely used to guide treatment. Although poor outcomes reported from clinical practice are multifactorial, availability of reliable, reproducible, and quantitative evaluation tools to accurately measure the fluid response i.e. a “VEGF meter” may be a better means of monitoring and treating than the current purely qualitative evaluation used in clinical practice. Design Post-hoc analysis of a phase III, randomized, multicenter study. Participants Study eyes of 1095 treatment-naive subjects receiving pro-re-nata ...

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    12. U-Net with Spatial Pyramid Pooling for Drusen Segmentation in Optical Coherence Tomography

      U-Net with Spatial Pyramid Pooling for Drusen Segmentation in Optical Coherence Tomography

      The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-guided management of AMD. There are two common approaches to drusen segmentation. In the first, the drusen are segmented directly as a binary classification task. In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch’s membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen. In this work, we extend the standard U-Net architecture with spatial pyramid pooling components to introduce global feature ...

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    13. RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

      RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

      Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams ...

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    14. 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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    15. Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images

      Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images

      The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SDOCT scans. Evaluation on annotated OCT images of the retina demonstrates that a residual U-Net allows to segment HRF with high accuracy. As ...

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    16. Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm

      Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm

      PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DESIGN: Retinal images from five Spectral-Domain Optical Coherence Tomography scanners were used to determine total retinal thickness with scanner-specific correction factors establishing consistency of thickness measurement across devices. PARTICIPANTS: 55 Fovea-centered Spectral-Domain Optical Coherence Tomography volumes from eleven subjects were analyzed, obtained from Cirrus HD-OCT, RS-3000, Heidelberg Spectralis, RTVue and Topcon2000, seven subjects with retinal diseases and four normal controls. METHOD: The Iowa Reference Algorithm measured total retinal thickness. Nonlinear model of total retinal thickness measurement comparisons was derived ...

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    17. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Image

      Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Image

      Purpose : To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. Methods : Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm 3 ) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate ...

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    18. Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation

      Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation

      Background/aims The purpose of the study was to create a standardised protocol for choroidal thickness measurements and to determine whether choroidal thickness measurements made on images obtained by spectral domain optical coherence tomography (SD-OCT) and swept source (SS-) OCT from patients with healthy retina are interchangeable when performed manually or with an automatic algorithm. Methods 36 grid cell measurements for choroidal thickness for each volumetric scan were obtained, which were measured for SD-OCT and SS-OCT with two methods on 18 eyes of healthy volunteers. Manual segmentation by experienced retinal graders from the Vienna Reading Center and automated segmentation on ...

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    19. Three-dimensional automated choroidal volume assessment on standard spectral-domain optical coherence tomography and correlation with the level of diabetic macular edema

      Three-dimensional automated choroidal volume assessment on standard spectral-domain optical coherence tomography and correlation with the level of diabetic macular edema

      Purpose To measure choroidal thickness on spectral-domain optical coherence tomography (SD-OCT) images using automated algorithms and to correlate choroidal pathology with retinal changes due to diabetic macular edema (DME). Design Post-hoc analysis of multicenter clinical trial baseline data. Methods SD-OCT raster scans/fluorescein angiograms were obtained from 284 treatment naïve eyes of 142 patients with clinically significant DME and from 20 controls. Three-dimensional (3D) SD-OCT images were evaluated by a certified independent reading center analyzing retinal changes associated with diabetic retinopathy. Choroidal thicknesses were analyzed using a fully automated algorithm. Angiograms were assessed manually. Multiple endpoint correction according to ...

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    20. Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography

      Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography

      When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graphtheoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2D en-face alignment, all the fields are segmented ...

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    1-20 of 20
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    Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography Three-dimensional automated choroidal volume assessment on standard spectral-domain optical coherence tomography and correlation with the level of diabetic macular edema Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Image Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images U-Net with Spatial Pyramid Pooling for Drusen Segmentation in Optical Coherence Tomography Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography Predicting Progression of Age-Related Macular Degeneration Using Optical Coherence Tomography and Fundus Photography Structural abnormalities associated with glaucoma using swept-source optical coherence tomography in patients with systemic sclerosis Clinical presentation does not affect acute mechanical performance of the Novolimus-eluting bioresorbable vascular scaffold as assessed by optical coherence tomography