1. Articles from Georg Langs

    1-10 of 10
    1. Computerized device and method for processing image data

      Computerized device and method for processing image data

      A computerized device for processing image data is proposed. The computerized device comprises a receiving unit which is configured to receive optical coherence tomography data of a of a tissue, in particular of a retina, a providing unit which is configured to provide a convolutional neural network for processing the optical coherence tomography data, and a processing unit which is configured to process the received optical coherence tomography data using the convolutional neural network for identifying at least one certain object in the tissue.

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    2. 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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    3. Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

      Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

      Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients’ response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and random forests-based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography (OCT) imaging ...

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    4. Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Image

      Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Image

      Abstract: Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients’ response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and Random Forests based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography ...

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    5. Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation

      Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation

      Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation ...

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    6. Improve synthetic retinal OCT images with present of pathologies and textural information

      Improve synthetic retinal OCT images with present of pathologies and textural information

      The lack of noise free Optical Coherence Tomography (OCT) images makes it challenging to quantitatively evaluate performance of image processing methods such as denoising methods. The synthetic noise free OCT images are needed to evaluate performance of image processing methods. The current synthetic methods fail to generate synthetic images that represent real OCT images with present of pathologies. They cannot correctly imitate real OCT data due to a tendency to smooth the data, losing texture information and even, pathologies such as cysts are simply smoothed away by these methods. The first aim of this paper is to use mathematical models ...

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    7. Geodesic denoising for optical coherence tomography images

      Geodesic denoising for optical coherence tomography images

      Optical coherence tomography (OCT) is an optical signal acquisition method capturing micrometer resolution, cross-sectional three-dimensional images. OCT images are used widely in ophthalmology to diagnose and monitor retinal diseases such as age-related macular degeneration (AMD) and Glaucoma. While OCT allows the visualization of retinal structures such as vessels and retinal layers, image quality and contrast is reduced by speckle noise, obfuscating small, low intensity structures and structural boundaries. Existing denoising methods for OCT images may remove clinically significant image features such as texture and boundaries of anomalies. In this paper, we propose a novel patch based denoising method, Geodesic Denoising ...

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    8. Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion

      Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion

      Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high- resolution, three-dimensional (3D) cross-sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD), glaucoma and retinal vein occlusion (RVO). Disease diagnosis, assessment, and treatment will require a patient to undergo multiple OCT scans, possibly using multiple scanners, to accurately and precisely gauge disease activity, progression and treatment success. However, cross-vendor imaging and patient movement may result in poor scan spatial correlation potentially leading to ...

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    9. Motion Artefact Correction in Retinal Optical Coherence Tomography Using Local Symmetry

      Motion Artefact Correction in Retinal Optical Coherence Tomography Using Local Symmetry

      Patient movements during the acquisition of SD-OCT scans create substantial motion artefacts in the volumetric data that hinder registration and 3D analysis and can be mistaken for pathologies. In this paper we propose a method to correct these artefacts using a single volume scan while still retaining the overall shape of the retina. The method was quantitatively validated using a set of synthetic SD-OCT volumes and qualitatively by a group of trained OCT grading experts on 100 SD-OCT scans. Furthermore, we compared the motion compensation estimation by the proposed method with a hardware eye tracker on 100 SD-OCT volumes.

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    10. Stable Registration of Pathological 3D SD-OCT Scans using Retinal Vessels

      Stable Registration of Pathological 3D SD-OCT Scans using Retinal Vessels

      We propose a multiple scanner vendor registration method for pathological retinal 3D spectral domain optical coherence tomography volumes based on Myronenko’s Coherent Point Drift and our automated vessel shadow segmentation. Coherent point drift is applied to the segmented retinal vessel point sets used as landmarks to generate the registration parameters required. In contrast to other registration methods, our solution incorporates a landmark detection and extraction method that specifically limits the extraction of false positives and a registration method capable of handling any such noise in the landmark point sets. Our experiments show modified Hausdorff distance is reduced by a ...

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    1-10 of 10
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    Stable Registration of Pathological 3D SD-OCT Scans using Retinal Vessels Motion Artefact Correction in Retinal Optical Coherence Tomography Using Local Symmetry Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion Geodesic denoising for optical coherence tomography images Improve synthetic retinal OCT images with present of pathologies and textural information Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Image Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images Computerized device and method for processing image data Distortion and Instability Compensation with Deep Learning for Rotational Scanning Endoscopic Optical Coherence Tomography Laser-induced choroidal neovascularization detected on optical coherence tomography angiography in patients with diabetic retinopathy