Part-based local shape models for colon polyp detection.
This paper presents a model-based technique for lesion detection in colon CT scans that uses analytical shape models to map the local shape curvature at individual voxels to anatomical labels. Local intensity profiles and curvature information have been previously used for discriminating between simple geometric shapes such as spherical and cylindrical structures. This paper introduces novel analytical shape models for colon-specific anatomy, viz. folds and polyps, built by combining parts with simpler geometric shapes. The models better approximate the actual shapes of relevant anatomical structures while allowing the application of model-based analysis on the simpler model parts. All parameters are derived from the analytical models, resulting in a simple voxel labeling scheme for classifying individual voxels in a CT volume. The algorithm's performance is evaluated against expert-determined ground truth on a database of 42 scans and performance is quantified by free-response receiver-operator curves.
Bhotika
Rahul
R
GE Global Research, One Research Circle, Niskayuna, NY 12309, USA. bhotika@research.ge.com
Mendonça
Paulo R S
PR
Sirohey
Saad A
SA
Turner
Wesley D
WD
Lee
Ying-Lin
YL
McCoy
Julie M
JM
Brown
Rebecca E B
RE
Miller
James V
JV
D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; 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