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Proceedings Paper

An MTANN CAD for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial: preliminary results
Author(s): Kenji Suzuki; Ivan Sheu; Mark Epstein; Ryan Kohlbrenner; Antonella Lostumbo; Don C. Rockey; Abraham H. Dachman
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Paper Abstract

A major challenge in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the detection of "difficult" polyps which radiologists are likely to miss. Our purpose was to develop a CAD scheme incorporating massive-training artificial neural networks (MTANNs) and to evaluate its performance on false-negative (FN) cases in a large multicenter clinical trial. We developed an initial polyp-detection scheme consisting of colon segmentation based on CT value-based analysis, detection of polyp candidates based on morphologic analysis, and quadratic discriminant analysis based on 3D pattern features for classification. For reduction of false-positive (FP) detections, we developed multiple expert 3D MTANNs designed to differentiate between polyps and seven types of non-polyps. Our independent database was obtained from CTC scans of 155 patients with polyps from a multicenter trial in which 15 medical institutions participated nationwide. Among them, about 45% patients received FN interpretations in CTC. For testing our CAD, 14 cases with 14 polyps/masses were randomly selected from the FN cases. Lesion sizes ranged from 6-35 mm, with an average of 10 mm. The initial CAD scheme detected 71.4% (10/14) of "missed" polyps, including sessile polyps and polyps on folds, with 18.9 (264/14) FPs per case. The MTANNs removed 75% (197/264) of the FPs without loss of any true positives; thus, the performance of our CAD scheme was improved to 4.8 (67/14) FPs per case. With our CAD scheme incorporating MTANNs, 71.4% of polyps "missed" by radiologists in the trial were detected correctly, with a reasonable number of FPs.

Paper Details

Date Published: 27 March 2008
PDF: 7 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150F (27 March 2008); doi: 10.1117/12.769824
Show Author Affiliations
Kenji Suzuki, The Univ. of Chicago (United States)
Ivan Sheu, The Univ. of Chicago (United States)
Mark Epstein, The Univ. of Chicago (United States)
Ryan Kohlbrenner, The Univ. of Chicago (United States)
Antonella Lostumbo, The Univ. of Chicago (United States)
Don C. Rockey, Univ. of Texas Southwestern Medical Ctr. (United States)
Abraham H. Dachman, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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