Abstract
Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select those image regions presenting the highest stable spatio-temporal response. Spatio-temporal stability is achieved by extracting 3D watershed regions on the temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of \(21.5\,\%\) in polyp localization and \(43.78\,\%\) in polyp detection.
Keywords
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Acknowledgments
Work supported by the Spanish project TIN2012-33116, DPI2015-65286-R, and the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya 2014-SGR-1470. Debora Gil is a Serra Hunter fellow.
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Gil, D., Javier Sánchez, F., Fernández-Esparrach, G., Bernal, J. (2016). 3D Stable Spatio-Temporal Polyp Localization in Colonoscopy Videos. In: Luo, X., Reichl, T., Reiter, A., Mariottini, GL. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2015. Lecture Notes in Computer Science(), vol 9515. Springer, Cham. https://doi.org/10.1007/978-3-319-29965-5_14
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