[2303.14711] Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2303.14711 (eess)
[Submitted on 26 Mar 2023]

Title:Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography

Authors:Marcel Reimann, Jungeun Won, Hiroyuki Takahashi, Antonio Yaghy, Yunchan Hwang, Stefan Ploner, Junhong Lin, Jessica Girgis, Kenneth Lam, Siyu Chen, Nadia K. Waheed, Andreas Maier, James G. Fujimoto
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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.
Comments: Accepted as poster at BVM workshop 2023 (this https URL). The arXiv version provides full quality figures. 6 pages content (2 figures)
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2303.14711 [eess.IV]
  (or arXiv:2303.14711v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2303.14711

Submission history

From: Marcel Reimann [view email]
[v1] Sun, 26 Mar 2023 12:45:42 UTC (818 KB)
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