P. C. De Groen

Polyp Detection in Colonoscopy Video using Elliptical Shape Feature

Early detection of polyps and cancers is one of the most important goals of colonoscopy. Computer-based analysis of video files using texture features, as has been proposed for polyps of the stomach and colon, has two major limitations: this method uses a fixed size analysis window and relies heavily on a training set of images for accuracy. To overcome these limitations, we propose a new technique focusing on shape instead of texture in this paper. The proposed polyp region detection method is based on the elliptical shape that is common for nearly all small colon polyps.

Computer-Aided Detection of Diagnostic and Therapeutic Operations in Colonoscopy Videos

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon and to perform - if deemed necessary - at the same time a number of diagnostic and therapeutic operations. In order to see the inside of the colon, a video signal of the internal mucosa of the colon is generated by a tiny video camera at the tip of the endoscope and displayed on a monitor for real-time analysis by the physician. We have captured and stored these videos in digital format and call these colonoscopy videos. Based on new algorithms for instrument detection and shot segmentation, we introduce new spatio-temporal analysis techniques to automatically identify an operation shot - a segment of visual data in a colonoscopy video that corresponds to a diagnostic or therapeutic operation. Our experiments on real colonoscopy videos demonstrate the effectiveness of the proposed approach. The proposed techniques and software are useful for 1) postprocedure review for causes of complications due to diagnostic or therapeutic operations; 2) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; 3) development of a systematic approach to assess and improve the procedural skills of endoscopists.

Measuring Objective Quality of Colonoscopy

Advances in video technology are being incorporated into todays 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.

A framework for parsing colonoscopy videos for semantic units

Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable for endoscopic research and education. The first necessary step for the analysis is parsing for semantic units. Since the characteristics of colonoscopy videos differ from those of videos studied in the literature, we introduce a new video parsing framework that includes: (i) a new scene definition and a new video parsing paradigm; (ii) a novel scene segmentation algorithm using audio analysis and finite state automata to recognize scenes and associated boundaries. Our experimental results show average precision and recall of 95% and 81%, respectively, for parsing scenes. The framework is extensible to videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.

Detection of Quality Visualization of Appendiceal Orifices Using Local Edge Cross-Section Profile Features and Near Pause Detection

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is an important quality indicator of the colon examination. In this paper, we present two new algorithms. The first algorithm determines whether an image shows the clearly seen appendiceal orifice. This algorithm uses our new local features based on geometric shape, illumination difference, and intensity changes along the norm direction (cross section) of an edge. The second algorithm determines whether the video is an appendix video (the video showing at least 3 s of the appendiceal orifice inspection). Such a video indicates good visualization of the appendiceal orifice. This algorithm utilizes frame intensity histograms to detect a near camera pause during the apendiceal orifice inspection. We tested our algorithms on 23 videos captured from two types of endoscopy procedures. The average sensitivity and specificity for the detection of appendiceal orifice images with the often seen crescent appendiceal orifice shape are 96.86% and 90.47%, respectively. The average accuracy for the detection of appendix videos is 91.30%.

Near Real-Time Retroflexion Detection in Colonoscopy

Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy helped to detect more polyps. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects retroflexion during colonoscopy. We propose region shape and location (RSL) features and edgeless edge cross-section profile (ECSP) features that encapsulate important properties of endoscope appearance and edge information during retroflexion. Our experimental results on 50 colonoscopy test videos show that a simple ensemble classifier using both ECSP and RSL features can effectively identify retroflexion in terms of analysis time and detection rate.

Quadrant Coverage Histogram: A New Method for Measuring Quality of Colonoscopic Procedures

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. Although millions of colonoscopic procedures are performed annually, an objective method that estimates how much effort was undertaken to insure maximal inspection of the visible parts of the inside of the colon, does not exist. Experts agree that it is desirable to inspect all quadrants of the colon wall while the endoscope is gradually withdrawn. In this paper, we present a new computer-based method that constructs a Quadrant Coverage Histogram to determine the number of quadrants of the colon wall inspected during the withdrawal phase of colonoscopy. The proposed method is part of our novel computer-aided quality control system for colonoscopy intended for use in routine clinical practice.