Abstract
Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all polyps under real time constraints, increasing its performance due to our adaptation strategy.
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Acknowledgements
The authors acknowledge the support of the following agencies for research funding: Spanish government through funded project iVENDIS (DPI2015-65286-R), SATT IdFInnov (France) through the Project Smart Videocolonoscopy under Grant 186, Catalan government through SGR projects 2014-SGR-1470 and 2014-SGR-135, CERCA Programme/Generalitat de Catalunya and FSEED.
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Angermann, Q. et al. (2017). Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis. In: Cardoso, M., et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_3
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DOI: https://doi.org/10.1007/978-3-319-67543-5_3
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