We present a software system called "Polyp-Alert" to assist the endoscopist find polyps by providing visual feedback during colonoscopy. Polyp-Alert employs our previous edge-cross-section visual features and a rule-based classifier to detect a polyp edge-an edge along the contour of a polyp. The technique employs tracking of detected polyp edge(s) to group a sequence of images covering the same polyp(s) as one polyp shot. In our experiments, the software correctly detected 97.7% (42 of 43) of polyp shots on 53 randomly selected video files of entire colonoscopy procedures. However, Polyp-Alert incorrectly marked only 4.3% of a full-length colonoscopy procedure as showing a polyp when they do not. The test data set consists of about 18. h worth of video data from Olympus and Fujinon endoscopes. The technique is extensible to other brands of colonoscopes. Furthermore, Polyp-Alert can provide as high as ten feedbacks per second for a smooth display of feedback. The performance of our system is by far the most promising to potentially assist the endoscopist find more polyps in clinical practice during a routine screening colonoscopy.
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation STTR Grant no. IIP-0956847 , EndoMetric Corporation , and Mayo Clinic in Rochester, MN. Findings, opinions, and conclusions expressed in this paper do not necessarily reflect the view of the funding agencies. Johnny Wong, Wallapak Tavanapong, and JungHwan Oh hold positions in EndoMetric Corporation, Ames, IA 50014, U.S.A, a for profit company that markets endoscopy-related software. De Groen is the medical advisor of EndoMetric.
- Medical imaging/video
- Near Real-time
- Polyp detection