Edge displacement field-based classification for improved detection of polyps in CT colonography.
Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).
Acar
Burak
B
Department of Radiology, LUCAS MRS Center, 3D Laboratory, Stanford University, Stanford, CA 94305, USA. bacar@stanford.edu
Beaulieu
Christopher F
CF
Göktürk
Salih B
SB
Tomasi
Carlo
C
Paik
David S
DS
Jeffrey
R Brooke
RB
Yee
Judy
J
Napel
Sandy
S
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003111:Colonic Polyps / Q000145:classification / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011786:Quality Control; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity