A 3D Approach for Computer-Aided Liver Lesion Detection

1. Reed Tompkins DePaul Medix Program 2008 Mentor: Kenji Suzuki, Ph.D. Special Thanks to Edmund Ng A 3D Approach for Computer-Aided Liver Lesion Detection

2. Presentation Outline • Background Information • Prior Research • Proposed Methodology • Liver Segmentation • HCC Candidate Detection • Results • Conclusions and Future Work

3. HCC Background • Hepatocellular Carcinoma • Primary Liver Cancer • Prevalence varies drastically by region • Few Symptoms • Usually affects people with preexisting liver conditions Background Information

4. HCC Background II • Estimated to cause at least 372,000 deaths annually • Other than CT imagery, difficult to detect • Difficult / time consuming for radiologists to spot Background Information

5. Project Background • 2D Lesion Detector program, “Candidate Finder 1.0,” written and tested in previous summer • Written in ITK – open source, C/C++ toolkit • CandidateFinder both segments liver and attempts to detect tumor candidates • 100% Sensitivity • Small Number of Test Cases Background Information

6. Project Background II • 2D Algorithm resulted in high number of false positives • On 2D Data: 24 FPs on average • On 3D Data: Hundreds of FPs • Program not written using object-oriented techniques • No way to view program intermediates Background Information

7. Project Goals • Develop a 3D computerized scheme for detection of hepatocellular carcinoma (HCC) in liver CT images • Modify and modularize existing liver lesion detection program Background Information

8. Data Set • 15 CT scans, with a total of 17 HCC tumors • Contrast-enhanced CT images; arterial phase • Resolution: 512 x 512 x (200 – 300)‏ • Spacing of Pixels = [0.67 mm, 0.67 mm, 0.62 mm] • Tumor centers identified by trained radiologist Background Information

9. Prior Research • Gletsos et al (2003)‏ • Used gray level and texture features to build a classifier for use in a neural network • Operated on 2D data, did not focus on HCC specifically • Tajima et al (2007)‏ • Used temporal subtraction and edge processing to detect HCC specifically • Required multiple “phases” of CT liver images to work Prior Research

10. Prior Research II • Shiraishi et al (2008)‏ • Used microflow imaging to build an HCC classifier • Microflow imaging is not approved by FDA • Used ultrasonography, not computer tomography • Watershed Algorithm • Huang et al (Breast Tumors)‏ • Marloes et al (Brain Tumors)‏ • Sheshadri et al (Breast Tumors)‏ Prior Research

11. Proposed Methodology – Liver Segmentation • Not a liver segmentation project, but important to do it correctly • Not terribly concerned with oversegmentation • Method suggested by ITK manual Liver Lesion Liver Lesion Proposed Methodology – Liver Segmentation

12. Overview of Liver Segmentation Proposed Methodology – Liver Segmentation

13. Liver Pre-Processing Proposed Methodology – Liver Segmentation

14. Fast Marching Segmenter Proposed Methodology – Liver Segmentation

15. Geodesic Active Contours Input Level Set Edge Image Proposed Methodology – Liver Segmentation

16. Binary Image Proposed Methodology – Liver Segmentation

17. Binary Liver Mask Two Different Binary Liver Masks Proposed Methodology – Liver Segmentation

18. Liver Segmentation Complete Two Different Segmented Livers Proposed Methodology – Liver Segmentation

19. Proposed Methodology – HCC Candidate Detection • Pre-process segmented liver • Apply watershed algorithm • Eliminate/consolidate watershed regions • Check distance from actual tumors Proposed Methodology – HCC Candidate Detection

20. HCC Candidates Pre Processing • Filter out noise from image • Alter pixel intensity • Sharpen/define edges Proposed Methodology – HCC Candidate Detection

21. Segmented Liver with Gradient Filter Applied Proposed Methodology – HCC Candidate Detection

22. HCC Candidates Pre Processing II • Calculate image statistics (used by watershed algorithm)‏ • Apply a half-thresholder (try to eliminate uninteresting regions)‏ Proposed Methodology – HCC Candidate Detection

23. Watershed Segmentation Conceptual Proposed Methodology – HCC Candidate Detection

24. Watershed Segmentation • In other words, the watershed algorithm locates the minimum intensity of regions, and keeps growing those enclosed regions until it encounters another growing region, or a boundary. • We used the watershed algorithm to find tumor candidates. Proposed Methodology – HCC Candidate Detection

25. QUIZ TIME!

26. QUIZ TIME! My program attempts to locate HCC within liver CT images. What does HCC stand for?

27. Results • How do we define “success”? • Centroid of 3D watershed region is less than 30 mm away from location of tumor (as marked by radiologist)‏ • Possible problem with this definition? Results

28. Results II • Average FPs = 14.2 FP, Average Distance = 12.6 mm Results

29. Watershed Output Original Image Sigmoid Watershed Distance = 0.47 mm Gradient

30. Watershed Output II Sigmoid Original Image Watershed Gradient

31. Conclusions • We have developed a 3D algorithm for the detection of HCC with 100% sensitivity on 15 test cases with a reasonable number of FPs. • We have successfully translated a 2D algorithm to 3D, with fewer false positives. • We have successfully modularized the program, allowing intermediates to be output. Conclusions and Future Work

32. Future Work • Modify program to help detect cancers other than HCC • Possibly integrate project with another student project • Add a false positive reducer (MTANN?) Conclusions and Future Work

33. Thanks! • Thanks Again To: • Kenji Suzuki, Ph.D. • Edmund Ng • DePaul Medix Program • And, of course… Contact Information: rtompkins@gonzaga.edu

34. Any Questions? Thanks To My Momma