Aggregating Optical Coherence Tomography (OCT) scan data and human expert annotations for hydrogel-treated wounds in a mouse model. It includes raw OCT scans and corresponding tissue annotations.
Use Cases
- Train a deep learning model on raw OCT scans with human expert annotations for automated tissue segmentation in wound healing studies.
- Analyze the correlation between OCT scan features and hydrogel treatment outcomes using the annotated tissue labels.
- Develop a multimodal analysis pipeline combining raw OCT scan data and human annotations to quantify in vivo healing dynamics.
Strengths
- Data includes human expert annotations for tissue in OCT scans, providing a ground truth for analysis.
- Dataset is focused on a specific biomedical application: hydrogel-treated wound healing in a mouse model.
Limitations
- The sample size and number of scans are unknown, which limits assessment of statistical power.
- The dataset's temporal coverage and geographic origin of the mouse model are unspecified.
Provenance
- Source
- Harvard Dataverse
- Collection Method
- Optical Coherence Tomography (OCT) scanning of hydrogel-treated wounds in a mouse model, with subsequent human expert annotation.
- Freshness
- Last updated on 2026-02-02.