A two-level approach towards semantic colon segmentation: removing extra-colonic findings.
Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.
Lu
Le
L
CAD & Knowledge Solutions, Siemens Healthcare, Malvern, PA 19355, USA.
Wolf
Matthias
M
Liang
Jianming
J
Dundar
Murat
M
Bi
Jinbo
J
Salganicoff
Marcos
M
D000465:Algorithms; D001185:Artificial Intelligence; D003106:Colon / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; 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; D013382:Subtraction Technique