Abstract
Colorectal cancer is the second most common cause of cancer death in the United States, with an estimated 140,000 new cases leading to 50,000 deaths this year. The best treatment is to detect and treat the cancer before it becomes invasive and spreads. The most common form of detection is the use of optical colonoscopy in which the clinician visually inspects the surface of the colon through an endoscope to detect the presence of polyps. Studies have shown that even the best clinicians will sometimes miss polyps, especially the more subtle flat polyps, and that many cancers that develop in the years immediately following a colonoscopy likely originate from missed polyps. In this paper we describe techniques for extracting several medically-driven features from colonoscopy video that can be used to detect the presence of flat polyps. Initial quantitative and qualitative results show that each of these features on their own provide some level of discrimination and, when combined, have the potential to support robust detection of flat polyps.
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The authors thank J. S. Marron for his helpful conversation on statistical issues.
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Fan, M., Vicory, J., McGill, S., Pizer, S., Rosenman, J. (2018). Features for the Detection of Flat Polyps in Colonoscopy Video. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_12
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DOI: https://doi.org/10.1007/978-3-319-95921-4_12
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