A Pipeline for Computer Aided Polyp Detection

1. A Pipeline for Computer Aided Polyp Detection Wei Hong, Feng Qiu, and Arie Kuafman Center for Visual Computing (CVC) and Department of Computer Science Stony Brook University

2. Related Work • Shape based polyp detection method • Vining et al. ’99: colon wall thickness • Tomasi et al. ’00: sphere fitting • Summers et al. ’01: local curvature variations • Yoshida et al. ’01: shape index and curvedness • Paik et al. ’04: intersecting normal vectors • Wang et al. ’06: global curvature • Sensitive to the irregularity of the colon wall • Relatively high false positive rate

3. Electronic Biopsy Transparent transfer function Opaque transfer function Polyps have a slightly higher density and different texture.

4. Overview of Our CAD Pipeline Contrast-enhanced CT Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

5. Step 1 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

6. Segmentation & Digital Cleansing • Input: contrast-enhanced CT scan of patient’s abdomen • Goal: segment and cleanse colon lumen • Challenges for digital cleansing: • Remove the interface layer between air and tagged fluid • Restore the CT densities in the enhanced mucosa layer interface air mucosa tagged fluid

7. Partial Volume Segmentation • Assumptions: • Four material classes within each voxel i (air, soft tissue, muscle, and bone) • Each material follows a Gaussian distribution • : the observed density value at voxel i • : Gaussian noise with zero mean • : fraction of classes k • Using expectation-maximization algorithm to estimate

8. Segmentation Results air bone air & fluid partial volume effect tissue & fluid partial volume effect

9. Digital Cleansing Results Original CT slice Cleansed slice Cleansing equation: Zoomed view

10. Step 2 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

11. Genus zero Colon Surface Extraction • Topological noise (i.e., tiny handles) • makes our flattening algorithm complex • introduces distortion • Simple point: A point is simple if its addition to and removal from objects does not change object topology. • Our 3D region growing based method • Computing a distance field • Computing colon centerline • Region growing all simple points Simple point Critical point

12. Step 3 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

13. Virtual Colon Flattening • Bartroli et al. ’01: area preserving • Haker et al. ’00: angle preserving • Genus 0 surfaces • Mapping to a planar parallelogram • Our Method: angle preserving • Surfaces with arbitrary topology • Mapping to a 2D rectangle

14. Conformal Colon Flattening • Computing a gradient field of the conformal map • Computing the conformal map by integration • Tracing a horizontal line • Cutting the colon surface along the horizontal line

15. Angle Preserving 3D cutting line 2D

16. Step 4 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

17. Electronic Biopsy Image Generation Pre-defined translucent transfer function Surface normals used as ray directions Rays are allowed to traverse up to 40 steps (0.5mm/step) Rays cannot enter colon lumen GPU acceleration (300ms for 4000X196 image)

18. Step 5 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

19. Polyp Detection by Clustering • For each pixel, we use the color information in its small neighborhood as the feature vector • Method: • PCA: reduce the dimension of the feature vectors • Clustering algorithm: classify each pixel • A labeling algorithm: extract the connected components • We only consider polyps with a diameter > 5mm, small components are removed.

20. Step 6 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

21. Reduction of False Positives • Shape features are exploited for polyp detection • Volumetric shape index & curvedness Yoshida et al. • The computation of these volumetric shape features is time consuming • We only do it at suspicious areas for FP reduction

22. Results of Clustering and False Positive Reduction Electronic biopsy image The result of our clustering algorithm The result of FP reduction

23. Step 7 Segmentation and Digital Cleansing Colon Surface Extraction Conformal Colon Flattening Electronic Biopsy Image Generation Polyp Detection by Clustering False Positive Reduction Integration with Virtual Colonoscopy

24. Integration with Virtual Colonoscopy • The extracted colon mesh is used to accelerate volumetric ray-casting • Colon mesh is projected onto the image plane • Empty space between image plane and colon wall is skipped • Frame rate: 17-20/sec for image size of 512X512 • Suspicious polyp candidates are highlighted in the endoscopic view to attract the attention of the radiologists • A flattened colon image is also provided • Suspicious polyp locations • Bookmarks

25. Enhanced Endoscopic View

26. The User Interface of Our CAD System

27. Datasets • 52 CT datasets from National Institute of Health (NIH) • 400~500 Raw DICOM images (512X512) • VC reports and videos • OC reports and videos • Pathology reports • 46 CT datasets from Stony Brook University Hospital (SB) • 400~500 Raw DICOM images (512X512) • VC reports and videos • OC reports • Pathology reports

28. Experimental Results 3.6GHz Pentium IV, 3G Ram, Quadro FX4500, 512^3

29. Conclusions A novel method for automatic polyp detection by integrating direct volume rendering with conformal colon flattening • 100% sensitive to polyps with a low FP rate • Highlighting the polyp locations • Enhancing the user interface of VC • Improve the efficiency and accuracy of VC

30. Future Work • Improving detection algorithm to further reduce FPs • Porting our CAD pipeline to a clinical VC system • Supine and prone registration • Applying our methods to other human organs • Blood vessel • Bladder

31. Questions? Thank You!