1. Articles from Martin Ehler

    1-3 of 3
    1. 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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    2. An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

      An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

      Segmenting anatomical structures such as the photoreceptor layer in retinal optical coherence tomography (OCT) scans is challenging in pathological scenarios. Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions. In this paper we propose to overcome this limitation by means of an augmented target loss function framework. We introduce a novel amplified-target loss that explicitly penalizes errors within the central area of the input images, based on the observation that most of the ...

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    3. Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors

      Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors
      We describe a novel reconstruction algorithm based on Principal Component Analysis (PCA) applied to multi-spectral imaging data. Using numerical phantoms, based on a two layered skin model developed previously, we found analytical expressions, which convert qualitative PCA results into quantitative blood volume and oxygenation values, assuming the epidermal thickness to be known. We also evaluate the limits of accuracy of this method when the value of the epidermal thickness is not known. We show that blood volume can reliably be extracted (less than 6% error) even if the assumed thickness deviates 0.04mm from the actual value, whereas the error ...
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    1-3 of 3
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  2. Topics in the News

    1. (3 articles) Medical University of Vienna
    2. (2 articles) Ursula Schmidt-Erfurth
    3. (1 articles) National Institutes of Health
    4. (1 articles) Lawrence Livermore National Laboratory
    5. (1 articles) Sebastian M. Waldstein
    6. (1 articles) Michael Pircher
    7. (1 articles) Christoph K. Hitzenberger
    8. (1 articles) Stavros G. Demos
    9. (1 articles) Joost Daemen
    10. (1 articles) Notal Vision
    11. (1 articles) Thorlabs
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    Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning Fantom Encore Sirolimus-eluting Bioresorbable Scaffold for Treatment of De-novo CAD: the ENCORE-I Study Assessment of Microcirculatory Dysfunction in Septic Shock Patients by OCTA OCT system used at home demonstrates potential for daily monitoring of AMD Optical Coherence Tomography Biomarkers of the Outer Blood—Retina Barrier in Patients with Diabetic Macular Oedema Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Multi-channel swept source optical coherence tomography concept based on photonic integrated circuits Assessment of Retinal Vessel Density in Adult-Onset Foveomacular Vitelliform Dystrophy by Optical Coherence Tomography Angiography In Vivo Evaluation of Tissue Protrusion by Using Optical Coherence Tomography and Coronary Angioscopy Immediately After Stent Implantation ICOOR 2020 - 8^ INTERNATIONAL CONGRESS ON OCT AND OCT (Virtual Conference). December 18-19, 2020