The incidence of cutaneous squamous cell carcinoma (SCC) has increased in recent years, while it remains as an understudied subject. Current methods for the diagnose of skin cancer are either invasive, present a low resolution, or images that are difficult to interpret. Full-field optical coherence tomography (FF-OCT) has proven its potential as a non-invasive imaging technique of epithelial tissue. The sub-micrometer resolution offered by FF-OCT is able to show fine features such as cell nuclei, blood vessels, and fibrous structures in the dermis. However, not many physicians are familiar with the images derived from this technique. Using a well-known mouse model of SCC, we acquired volumetric data from skin samples at different stages of the disease using our home-made FF-OCT system. We present OCT-Mouse Skin (OCT-MoS) dataset, a curated dataset of 3400 annotated tomograms distributed into four classes, representing different stages in the development of SCC: normal, dysplasia, papilloma, and SCC. This dataset has a high potential for reuse in areas such as cancer research, medical training, image processing, and deep learning.
Calderon-Delgado M, Lin MY, Tjiu JW, Huang SL
idr0098-huang-octmos/experimentA ()
idr0098-huang-octmos/experimentB ()
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Copyright: Calderon-Delgado et al
Data Publisher: University of Dundee
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