Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a need for automatic detection arises. Labeled datasets are often not publicly available and there are usually large variations in scan protocols and scanner types. Thus, this work focuses on an unsupervised approach that is based on local peak-detection and random walker segmentation to detect small features on each B-scan of the volume.
Originalsprog | Engelsk |
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Titel | Bildverarbeitung für die Medizin 2023 Proceedings, German Workshop on Medical Image Computing, Braunschweig |
Redaktører | Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff |
Antal sider | 6 |
Forlag | Springer Science and Business Media Deutschland GmbH |
Publikationsdato | 2023 |
Sider | 232-237 |
ISBN (Trykt) | 9783658416560 |
DOI | |
Status | Udgivet - 2023 |
Eksternt udgivet | Ja |
Begivenhed | Bildverarbeitung für die Medizin Workshop, BVM 2023 - Braunschweig, Tyskland Varighed: 2 jul. 2023 → 4 jul. 2023 |
Konference
Konference | Bildverarbeitung für die Medizin Workshop, BVM 2023 |
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Land | Tyskland |
By | Braunschweig |
Periode | 02/07/2023 → 04/07/2023 |
Navn | Informatik aktuell |
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ISSN | 1431-472X |
Bibliografisk note
Publisher Copyright:
© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.
ID: 372187171