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Mixture of expert 3D massive‐training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography
Author(s) -
Suzuki Kenji,
Yoshida Hiroyuki,
Näppi Janne,
Armato Samuel G.,
Dachman Abraham H.
Publication year - 2008
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 - 10.1118/1.2829870
Subject(s) - cad , virtual colonoscopy , false positive paradox , colonoscopy , artificial intelligence , computer aided diagnosis , medicine , radiology , computer science , colorectal cancer , cancer , engineering drawing , engineering
One of the major challenges in computer‐aided detection (CAD) of polyps in CT colonography (CTC) is the reduction of false‐positive detections (FPs) without a concomitant reduction in sensitivity. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CAD as a useful tool. Major sources of FPs generated by CAD schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra‐colonic structures such as the small bowel and stomach. Our purpose in this study was to develop a method for the removal of various types of FPs in CAD of polyps while maintaining a high sensitivity. To achieve this, we developed a “mixture of expert” three‐dimensional (3D) massive‐training artificial neural networks (MTANNs) consisting of four 3D MTANNs that were designed to differentiate between polyps and four categories of FPs: (1) rectal tubes, (2) stool with bubbles, (3) colonic walls with haustral folds, and (4) solid stool. Each expert 3D MTANN was trained with examples from a specific non‐polyp category along with typical polyps. The four expert 3D MTANNs were combined with a mixing artificial neural network (ANN) such that different types of FPs could be removed. Our database consisted of 146 CTC datasets obtained from 73 patients whose colons were prepared by standard pre‐colonoscopy cleansing. Each patient was scanned in both supine and prone positions. Radiologists established the locations of polyps through the use of optical‐colonoscopy reports. Fifteen patients had 28 polyps, 15 of which were 5 – 9 mm and 13 were 10 – 25 mm in size. The CTC cases were subjected to our previously reported CAD method consisting of centerline‐based extraction of the colon, shape‐based detection of polyp candidates, and a Bayesian‐ANN‐based classification of polyps. The original CAD method yielded 96.4% ( 27 ∕ 28 ) by‐polyp sensitivity with an average of 3.1 ( 224 ∕ 73 ) FPs per patient. The mixture of expert 3D MTANNs removed 63% ( 142 ∕ 224 ) of the FPs without the loss of any true positive; thus, the FP rate of our CAD scheme was improved to 1.1 ( 82 ∕ 73 ) FPs per patient while the original sensitivity was maintained. By use of the mixture of expert 3D MTANNs, the specificity of a CAD scheme for detection of polyps in CTC was substantially improved while a high sensitivity was maintained.