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    <title>Visual analytics methods for retinal layers in optical coherence tomography data</title>
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  <abstract type="Summary">Optical coherence tomography is an important imaging technology for the early detection of ocular diseases. Yet, identifying substructural defects in the 3D retinal images is challenging. We therefore present novel visual analytics methods for the exploration of small and localized retinal alterations. Our methods reduce the data complexity and ensure the visibility of relevant information. The results of two cross-sectional studies show that our methods improve the detection of retinal defects, contributing to a deeper understanding of the retinal condition at an early stage of disease.&lt;eng&gt;</abstract>
  <abstract type="Summary">Die optische Kohärenztomographie ist ein wichtiges Bildgebungsverfahren zur Früherkennung von Augenerkrankungen. Die Identifizierung von substrukturellen Defekten in den 3D-Netzhautbildern ist jedoch eine Herausforderung. Wir stellen daher neue Visual-Analytics-Methoden zur Exploration von kleinen und lokalen Netzhautveränderungen vor. Unsere Methoden reduzieren die Datenkomplexität und gewährleisten die Sichtbarkeit relevanter Informationen. Die Ergebnisse zweier Querschnittsstudien zeigen, dass unsere Methoden die Erkennung von Netzhautdefekten in frühen Krankheitsstadien verbessern.&lt;ger&gt;</abstract>
  <note type="statement of responsibility">vorgelegt von Martin Röhlig</note>
  <note>GutachterInnen: Heidrun Schumann (Universität Rostock) ; Bernhard Preim (Otto-von-Guericke-Universität Magdeburg)</note>
  <note type="thesis">Dissertation Universität Rostock 2021</note>
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      <title>Visual analytics methods for retinal layers in optical coherence tomography data</title>
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      <publisher>Rostock, 2020</publisher>
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      <namePart>Röhlig, Martin, 1984 - </namePart>
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