Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
This work addresses the need for automatic detection of new biomarkers in retinal diseases, which is crucial for investigating disease progression and treatment outcomes, but it is incremental as it applies existing techniques to a specific domain challenge.
The paper tackled the problem of automatically detecting small hyperreflective features in ultrahigh resolution optical coherence tomography scans, which are potential biomarkers for retinal diseases like age-related macular degeneration, by developing an unsupervised method based on local peak-detection and random walker segmentation to reliably identify these features in 3D volumes without requiring labeled datasets.
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.