S Lakare, A Barbu, M Dundar, M Wolf, L Bogoni, D Comaniciu

1. S Lakare, A Barbu, M Dundar, M Wolf, L Bogoni, D Comaniciu Computer-Aided Detection and Knowledge Solutions Siemens Medical Solutions USA, Inc. Learning-based Component for Suppression of Rectal Tube False Positives: Evaluation of Performance on 780 CTC Cases

2. Motivation • Removing CAD marks on Rectal Tubes can decrease reviewing time spent on obvious false positives • Fewer obvious false positive marks can increase radiologists’ confidence of the CAD system

3. Marks on Rectal Tubes • Rectal tubes can have a bumpy, polyp-like shape • A CAD system can detect those bumps – resulting in false-positives (FPs)

4. Overview – Rectal Tube Detection Module 3D circles Input volume Output Short tubes Segmented tube

5. 3D Circle Detection • 3D curvature, gradient and data based features • 12 circles (4 radii x 3 relative positions) relative to the circle of interest • 8 types of statistics (mean, variance, percentiles, etc) • in total 6720 features • 15,000 positive samples • 207,000 negative samples • Detection Rate: 95.6%

6. Short Tube Detection • Short tubes are constructed from pairs of 3D circles of well aligned tubes • 13,700 positive samples • 400,000 negative samples • Detection Rate: 95.1% • The tubes are then connected by dynamic programming The parameters of a short tube A short tube is constructed from a pair of 3D circles

7. Training Data • Cases with clean prep • 234 volumes • 8 sites • Siemens, GE, Toshiba MDCT • 4, 16 and 64 slice scanners • Cases with tagged prep (combinations of iodine & barium) • 154 volumes • 4 sites • Siemens and GE MDCT • 16 and 64 slice scanners • Rectal Tubes are annotated and then used for training

8. Results – Standalone System • Tested on 210 unseen cases • Detection Rate: 94.7% • 26 false positives • 0.12 FP/vol • Running time was 5.3 seconds/volume • None of the 26 false alarms was a polyp

9. Detection Results

10. False Positives

11. Integration into CAD Prototype* Input Data Candidate Generation Feature Computation Classification CAD marks * Work in Progress, not available commercially

12. Integration into CAD Prototype Input Data Candidate Generation Feature Computation Classification Rectal Tube Detection CAD marks

13. Test Data • Cases with clean prep • 405 cases, 783 volumes • 10 sites • Siemens, GE, Toshiba MDCT • 4, 16 and 64 slice scanners • Cases with tagged prep (combinations of iodine & barium) • 373 cases, 587 volumes • 4 sites • Siemens and GE MDCT • 16 and 64 slice scanners

14. Integrated Results – CG Stage Input Data Candidate Generation Rectal Tube Detection CAD marks

15. Integrated Results – CG Stage • Clean cases • 257/405 had candidates on rectal tube (351/783 volumes) • Candidates/patient count reduced by 2.92 • Candidates/volume count reduced by 2.04 • Tagged cases • All volumes had candidates on rectal tube • Candidates/patient count reduced by 2.56 • Candidates/volume count reduced by 1.70

16. Integrated Results – Overall Input Data Candidate Generation Feature Computation Classification Rectal Tube Detection CAD marks

17. Integrated Results – Overall • Clean cases • Candidates/patient count reduced by 0.30 (8%) • Candidates/volume count reduced by 0.20 (10%) • Tagged cases • Candidates/patient count reduced by 0.15 (3%) • Candidates/volume count reduced by 0.09 (3%)

18. Conclusion • Presented a Rectal Tube detection method • CAD marks on rectal tubes are suppressed • Reduction in false positives • Can potentially reduce interpretation time for Radiologists • The system does not miss any additional polyps