  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.
