Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography

Reimann M, Won J, Takahashi H, Yaghy A, Hwang Y, Ploner S, Lin J, Girgis J, Lam K, Chen S, Waheed NK, Maier A, Fujimoto JG (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 232-237

Conference Proceedings Title: Informatik aktuell

Event location: Braunschweig DE

ISBN: 9783658416560

DOI: 10.1007/978-3-658-41657-7_50

Abstract

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.

Authors with CRIS profile

Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Massachusetts Institute of Technology (MIT) US United States (USA) (US) Tufts Medical Center US United States (USA) (US)

How to cite

APA:

Reimann, M., Won, J., Takahashi, H., Yaghy, A., Hwang, Y., Ploner, S.,... Fujimoto, J.G. (2023). Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography. In Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 232-237). Braunschweig, DE: Springer Science and Business Media Deutschland GmbH.

MLA:

Reimann, Marcel, et al. "Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2023, Braunschweig Ed. Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2023. 232-237.

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