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

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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


Motivation
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


Marks on rectal tubes
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)


Overview rectal tube detection module
Overview – Rectal Tube Detection Module

3D circles

Input volume

Output

Short tubes

Segmented tube


3d circle detection
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%


Short tube detection
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


Training data
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


Results standalone system
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




Integration into cad prototype
Integration into CAD Prototype*

Input Data

Candidate Generation

Feature Computation

Classification

CAD marks

* Work in Progress, not available commercially


Integration into cad prototype1
Integration into CAD Prototype

Input Data

Candidate Generation

Feature Computation

Classification

Rectal Tube Detection

CAD marks


Test data
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


Integrated results cg stage
Integrated Results – CG Stage

Input Data

Candidate Generation

Rectal Tube Detection

CAD marks


Integrated results cg stage1
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


Integrated results overall
Integrated Results – Overall

Input Data

Candidate Generation

Feature Computation

Classification

Rectal Tube Detection

CAD marks


Integrated results overall1
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%)


Conclusion
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


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