Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT.
We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.
Paik
David S
DS
Department of Radiology, Stanford University, Stanford, CA 94305-5450, USA. paik@smi.stanford.edu
Beaulieu
Christopher F
CF
Rubin
Geoffrey D
GD
Acar
Burak
B
Jeffrey
R Brooke
RB
Yee
Judy
J
Dey
Joyoni
J
Napel
Sandy
S
D000465:Algorithms; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D016037:Single-Blind Method; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D036542:Tomography, Spiral Computed / Q000379:methods*