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.
@article{arxiv.2303.14711,
title = {Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography},
author = {Marcel Reimann and Jungeun Won and Hiroyuki Takahashi and Antonio Yaghy and Yunchan Hwang and Stefan Ploner and Junhong Lin and Jessica Girgis and Kenneth Lam and Siyu Chen and Nadia K. Waheed and Andreas Maier and James G. Fujimoto},
journal= {arXiv preprint arXiv:2303.14711},
year = {2023}
}
Comments
Accepted as poster at BVM workshop 2023 (https://www.bvm-workshop.org/). The arXiv version provides full quality figures. 6 pages content (2 figures)