title: Hybrid segmentation of colon filled with air and opacified fluid for CT colonography.
abstract: Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.
authors.affiliation: Diagnostic Radiology Department, Warren Grant Magnuson Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA. mfranaszek@cc.nih.gov
authors.forename: Marek
authors.initials: M
authors.lastname: Franaszek
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
authors.forename: Perry J
authors.initials: PJ
authors.lastname: Pickhardt
authors.forename: J Richard
authors.initials: JR
authors.lastname: Choi
mesh_terms: D000388:Air; D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003287:Contrast Media; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media