PURPOSE: To develop a computer-aided diagnosis (CAD) scheme for automated detection of colonic polyps on the basis of volumetric features and to assess its accuracy on the basis of colonoscopy, the standard. MATERIALS AND METHODS: Computed tomographic (CT) colonography was performed in patients with use of standard bowel cleansing, air insufflation, and helical scanning in supine and prone positions. The colon was extracted from volumetric data sets generated from transverse CT sections. Volumetric features characterizing polyps were computed at each point in the extracted colon. Polyps were detected by means of hysteresis thresholding and fuzzy clustering followed by a rule-based test on the basis of feature values. Locations of the detected polyps were compared with those detected at conventional colonoscopy. RESULTS: Forty-one cases were analyzed: nine cases with polyps and 32 without polyps. Each case with polyps had one polyp of clinically important size (six were 5-9 mm; three, 10 mm). Thus, there were 82 volumetric data sets, 18 included polyps. Eighty-nine percent (16 of 18) of the polyps were detected. Each of the two false-negative findings was detected in the other position; thus, 100% of polyp cases were detected, with 2.5 false-positive findings per patient. The false-positive findings were similar to those due to common perceptual errors. Most of the false-positive findings were easily distinguishable from true polyps by experienced radiologists. CONCLUSION: The CAD scheme has the potential to depict polyps with high sensitivity and an acceptable false-positive rate.
PURPOSE: To develop a computer-aided diagnosis (CAD) scheme for automated detection of colonic polyps on the basis of volumetric features and to assess its accuracy on the basis of colonoscopy, the standard. MATERIALS AND METHODS: Computed tomographic (CT) colonography was performed in patients with use of standard bowel cleansing, air insufflation, and helical scanning in supine and prone positions. The colon was extracted from volumetric data sets generated from transverse CT sections. Volumetric features characterizing polyps were computed at each point in the extracted colon. Polyps were detected by means of hysteresis thresholding and fuzzy clustering followed by a rule-based test on the basis of feature values. Locations of the detected polyps were compared with those detected at conventional colonoscopy. RESULTS: Forty-one cases were analyzed: nine cases with polyps and 32 without polyps. Each case with polyps had one polyp of clinically important size (six were 5-9 mm; three, 10 mm). Thus, there were 82 volumetric data sets, 18 included polyps. Eighty-nine percent (16 of 18) of the polyps were detected. Each of the two false-negative findings was detected in the other position; thus, 100% of polyp cases were detected, with 2.5 false-positive findings per patient. The false-positive findings were similar to those due to common perceptual errors. Most of the false-positive findings were easily distinguishable from true polyps by experienced radiologists. CONCLUSION: The CAD scheme has the potential to depict polyps with high sensitivity and an acceptable false-positive rate.
Authors: Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan Journal: Med Phys Date: 2013-08 Impact factor: 4.071
Authors: Jiang Li; Adam Huang; Jack Yao; Jiamin Liu; Robert L Van Uitert; Nicholas Petrick; Ronald M Summers Journal: Med Phys Date: 2009-01 Impact factor: 4.071