Real-Time Polyp Detection in Colonoscopy Videos. : A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis

Abstract : Purpose: Colorectal cancer is the second leading cause of cancer death in United States when men and women are combined. Its incidence can be mitigated by detecting its precursor lesion, the polyp, before it develops into cancer. Colonoscopy is still the gold standard for colon screening though some polyps are still missed. Several computational systems have already been proposed to assist clinicians in this task but none of them is actually used in the exploration room due to not meeting real time constraints and not being tested under actual interventional sequences, compulsory to being of actual clinical use. Method: We present in this paper a real time polyp detection method built by adapting an existing frame{learning-based detection system to full sequences analysis; adaptation involves the use of more computationally ecient feature descriptors and the incorporation of spatio-temporal stability in method's response. We validate our methodology over a new fully public annotated video database and under clinical and technical criteria. Results Results show that our approach is able to detect all di erent polyps in the 18 video sequences that were considered while meeting real time constraints. More precisely, we study the impact of the choice of local feature descriptor (LBP and Haar) in the overall performance when considering usual metrics and how performance can be improved by considering a strengthening strategy in the learning process, by using spatio-temporal coherence and by combining di erent types of local features. ConclusionWork presented in this paper shows a strategy to adapt a still-frame-based polyp detection to video analysis. By analyzing the performance of our system we have also discovered potential future improvements related to the preprocessing of the frames extracted from the video. We also provide a full methodology to assess performance of a given method considering both clinical usability metrics and more usual ones in machine learning.
Type de document :
Communication dans un congrès
International Journal of Computer-Assisted Radiology and Surgery, Jun 2017, Heidelberg, Germany. Springer, Proceedings of CARS conference
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https://hal.archives-ouvertes.fr/hal-01488657
Contributeur : Aymeric Histace <>
Soumis le : lundi 13 mars 2017 - 22:02:36
Dernière modification le : vendredi 17 mars 2017 - 17:32:23

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  • HAL Id : hal-01488657, version 1

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Quentin Angermann, Jorge Bernal, Cristina Sánchez-Montes, Maroua Hammami, Gloria Fernández-Esparrach, et al.. Real-Time Polyp Detection in Colonoscopy Videos. : A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis. International Journal of Computer-Assisted Radiology and Surgery, Jun 2017, Heidelberg, Germany. Springer, Proceedings of CARS conference. <hal-01488657>

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