Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography.
OBJECTIVE
We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon.


METHODS
The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique.


RESULTS
On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10-15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate.


CONCLUSIONS
The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.
Näppi
Janne
J
Department of Radiology, The University of Chicago, Il 60637, USA. jnappi@uchicago.edu
Dachman
Abraham H
AH
MacEneaney
Peter
P
Yoshida
Hiroyuki
H
D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D007413:Intestinal Mucosa / Q000000981:diagnostic imaging; D014584:User-Computer Interface