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CT colonography: Advanced computer‐aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial
Author(s) -
Suzuki Kenji,
Rockey Don C.,
Dachman Abraham H.
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3263615
Subject(s) - computer aided diagnosis , cad , false positive paradox , artificial intelligence , virtual colonoscopy , medicine , radiology , pattern recognition (psychology) , computer science , colonoscopy , colorectal cancer , cancer , engineering drawing , engineering
Purpose The purpose of this study was to develop an advanced computer‐aided detection (CAD) scheme utilizing massive‐training artificial neural networks (MTANNs) to allow detection of “difficult” polyps in CT colonography (CTC) and to evaluate its performance on false‐negative (FN) CTC cases that radiologists “missed” in a multicenter clinical trial. Methods The authors developed an advanced CAD scheme consisting of an initial polyp‐detection scheme for identification of polyp candidates and a mixture of expert MTANNs for substantial reduction in false positives (FPs) while maintaining sensitivity. The initial polyp‐detection scheme consisted of (1) colon segmentation based on anatomy‐based extraction and colon‐based analysis and (2) detection of polyp candidates based on a morphologic analysis on the segmented colon. The mixture of expert MTANNs consisted of (1) supervised enhancement of polyps and suppression of various types of nonpolyps, (2) a scoring scheme for converting output voxels into a score for each polyp candidate, and (3) combining scores from multiple MTANNs by the use of a mixing artificial neural network. For testing the advanced CAD scheme, they created a database containing 24 FN cases with 23 polyps (range of 6 – 15 mm ; average of 8 mm ) and a mass ( 35 mm ) , which were “missed” by radiologists in CTC in the original trial in which 15 institutions participated. Results The initial polyp‐detection scheme detected 63% ( 15 ∕ 24 ) of the missed polyps with 21.0 ( 505 ∕ 24 ) FPs per patient. The MTANNs removed 76% of the FPs with loss of one true positive; thus, the performance of the advanced CAD scheme was improved to a sensitivity of 58% ( 14 ∕ 24 ) with 8.6 ( 207 ∕ 24 ) FPs per patient, whereas a conventional CAD scheme yielded a sensitivity of 25% at the same FP rate (the difference was statistically significant). Conclusions With the advanced MTANN CAD scheme, 58% of the polyps missed by radiologists in the original trial were detected and with a reasonable number of FPs. The results suggest that the use of an advanced MTANN CAD scheme may potentially enhance the detection of “difficult” polyps.