Optical Coherence Tomography (OCT) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness. We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo, rapid, wide-field (up to 90 x 2.5mm2) imaging in the oral cavity. Due to the size and complexity of these OCT data sets, rapid automated image processing software that immediately displays important tissue features is required to facilitate prompt bed-side clinical decisions.
We present an automated segmentation algorithm capable of detecting the epithelial surface and basement membrane in 3D OCT images of the oral cavity. The algorithm was trained using volumetric OCT data acquired in vivo from a variety of tissue types and histology-confirmed pathologies spanning normal through cancer (8 sites, 21 patients). The algorithm was validated using a second dataset of similar size and tissue diversity. We demonstrate application of the algorithm to an entire OCT volume to map epithelial thickness, and detection of the basement membrane, over the tissue surface. These maps may be clinically useful for delineating pre-surgical tumor margins, or for biopsy site guidance.
Ryan N. Goldan, Anthony M. D. Lee, Lucas Cahill, Kelly Liu, Calum MacAulay, Catherine F. Poh, and Pierre Lane, "Automated segmentation of oral mucosa from wide-field OCT images (Conference Presentation)," Proc. SPIE 9698, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIV, 96980R (Presented at SPIE BiOS: February 15, 2016; Published: 27 April 2016); https://doi.org/10.1117/12.2211122.4828055419001.
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