A large number of false positives (FPs) generated by computer-aided detection (CAD) schemes is likely to distract radiologists’ attention and decrease their interpretation efficiency. This study aims to develop projection-based features which characterize true and false positives to increase the specificity while maintaining high sensitivity in detecting colonic polyps.Methods:
In this study, two-dimensional projection images are obtained from each initial polyp candidate or volume of interest, and features are extracted from both the gray and color projection images to differentiate FPs from true positives. These projection features were tested to exclude different types of FPs, such as haustral folds, rectal tubes, and residue stool using a database of 325 patient studies (from two different institutions), which includes 556 scans at supine and/or prone positions with 347 polyps and masses sized from 5 to 60 mm. For comparison, several well-established features were used to generate a baseline reference. The experimental evaluation was conducted for large polyps and medium-sized polyps (5–9 mm) separately.Results:
For large polyps, the additional usage of the projection features reduces the FP rate from 5.31 to 1.92 per scan at the comparable by-polyp sensitivity level of 93.1%. For medium-sized polyps, the FP rate is reduced from 8.89 to 5.23 at the sensitivity level of 80.6%. The percentages of FP reduction are 63.9% and 41.2% for the large and medium-sized polyps, respectively, without sacrificing detection sensitivity.Conclusions:
The results have demonstrated that the new projection features can effectively reduce the FPs and increase the detection specificity without sacrificing the sensitivity. CAD of colonic polyps is supposed to help radiologists to improve their performance in interpreting computed tomographic colonography images.
This work was partially supported by NIH Grant Nos. CA082402 and CA120917 of the National Cancer Institute. Professor Hongbing Lu was supported by National Nature Science Foundation of China under Grant No. 6.772020. The authors would like to acknowledge the use of the Viatronix V3D-Colon Module, the helpful discussion with Dr. Chaijie Duan, PhD and Dr. Seth Mankes, MD, and the assistance from Dr. Hongyu Lu, PhD on data processing.
II.B. Overview of CADpolyp scheme
II.C. LRF and subvolume of an IPC
II.D. Acquisition of projection images
II.E. Features based on gray projection images
II.F. Features based on color projection images
III. DESIGN FOR PERFORMANCE EVALUATION
III.B. Feature extraction
IV.A. Overall performance
IV.B. False positive analysis
V. DISCUSSION AND CONCLUSION
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