Advances in video technology are being incorporated into today's healthcare practices. Colonoscopy is regarded as one of the most important diagnostic tools for colorectal cancer. Indeed, colonoscopy has contributed to a decline in the number of colorectal-cancer-related deaths. Although colonoscopy has become the preferred screening modality for prevention of colorectal cancer, recent data suggest that there is a significant miss rate for the detection of large polyps and cancers, and methods to investigate why this occurs are needed. To address this problem, we present a new computer-based method that analyzes a digitized video file of a colonoscopic procedure and produces a number of metrics that likely reflect the quality of the procedure. The method consists of a set of novel image-processing algorithms designed to address new technical challenges due to uncommon characteristics of videos captured during colonoscopy. As these measurements can be obtained automatically, our method enables future quality control in large-scale day-to-day medical practice, which is currently not feasible. In addition, our method can be adapted to other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, and bronchoscopy. Last but not least, our method may be useful to assess progress during colonoscopy training.
Bibliographical noteFunding Information:
Manuscript received October 15, 2007; revised May 23, 2008. First published October 7, 2008; current version published August 14, 2009. This work was supported in part by the National Science Foundation (NSF) under Grant IIS-0513777, Grant IIS-0513809, and Grant IIS-0513582, by the Mayo Clinic, by the Iowa Grow Values Fund, and by the NSF under Grant STTR-0740596. Asterisk indicates corresponding author.
- Quality measurement metrics
- Therapeutic and diagnostic operation detection
- Video segmentation