Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis

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.

[1]  Charles J. Lightdale,et al.  The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to December 1, 2002. , 2003, Gastrointestinal endoscopy.

[2]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[3]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[4]  A. M. Leufkens,et al.  Factors influencing the miss rate of polyps in a back-to-back colonoscopy study , 2012, Endoscopy.

[5]  Jung-Hwan Oh,et al.  Polyp-Alert: Near real-time feedback during colonoscopy , 2015, Comput. Methods Programs Biomed..

[6]  Til Aach,et al.  A comparison of blood vessel features and local binary patterns for colorectal polyp classification , 2009, Medical Imaging.

[7]  H. Tajiri,et al.  Narrow-band imaging in the diagnosis of colorectal mucosal lesions: a pilot study. , 2004, Endoscopy.

[8]  Changshui Zhang,et al.  Efficient Active Learning with Boosting , 2009, SDM.

[9]  Aymeric Histace,et al.  Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge , 2017, IEEE Transactions on Medical Imaging.

[10]  Nima Tajbakhsh,et al.  Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[11]  F. Prat,et al.  Computed virtual chromoendoscopy system (FICE): a new tool for upper endoscopy? , 2008, Gastroenterologie clinique et biologique.

[12]  M J Bruno,et al.  Magnification endoscopy, high resolution endoscopy, and chromoscopy; towards a better optical diagnosis , 2003, Gut.

[13]  Sun Young Park,et al.  Colonoscopic polyp detection using convolutional neural networks , 2016, SPIE Medical Imaging.

[14]  Aymeric Histace,et al.  Active Learning for Real Time Detection of Polyps in Videocolonoscopy , 2016, Annual Conference on Medical Image Understanding and Analysis.