1. Image Analysis for Neuroblastoma Classification: Hysteresis Thresholding for Nuclei Segmentation Metin Gurcan 1, PhD Tony Pan 1, MS Hiro Shimada 2, MD, PhD Joel Saltz 1, MD, PhD 1 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 2 Children’s Hospital, Los Angeles, CA gurcan.1@osu.edu www.bmi.osu.edu

2. CAD Computer-aided diagnosis: –a diagnosis made by a physician using the output of a computerized system Computerized system –Automated image (or data) analysis

3. Applications Breast Cancer Lung Cancer Colon Cancer

4. Observational Lapses Fatigue Distraction Emotional stress Satisfaction of Search Variation in reader

5. CAD Physician Decision

6. Breast Cancer M. N. Gurcan, B. Sahiner, H. P. Chan, L. Hadjiiski, and N. Petrick, "Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques," Med Phys, vol. 28, pp. 1937-48, 2001.

7. Lung Cancer M. N. Gurcan, B. Sahiner, N. Petrick, H. P. Chan, E. A. Kazerooni, P. N. Cascade, and L. Hadjiiski, "Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system," Med Phys, vol. 29, pp. 2552-8, 2002.

8. Nodule Segmentation M. N. Gurcan, B. H. Allen, S. K. Rogers, D. Dozer, R. Burns, and J. Hoffmeister, "Accurate nodule volume estimation from helical CT images: Comparison of slice-based and volume- based methods," 88th Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), 2002.

9. Polyp Segmentation M. Gurcan, R. Ernst, A. Oto, S. Worrell, J. Hoffmeister, and S. K. Rogers, "Measurement of colonic polyp size from virtual colonoscopy studies: Comparison of manual and automated methods," SPIE Medical Imaging Conference, vol. 6144, 2006.

10. Measurement M. Gurcan, R. Ernst, A. Oto, S. Worrell, J. Hoffmeister, and S. K. Rogers, "Measurement of colonic polyp size from virtual colonoscopy studies: Comparison of manual and automated methods," SPIE Medical Imaging Conference, vol. 6144, 2006.

11. NB Image Analysis Image Analysis Pathologist Decision

12. NB Image Analysis Image Analysis Pathologist Decision

13. Neuroblastoma Classification Stroma Density Differentiation Mitosis Karyorrhexis Index

14. Identify stroma density Stroma poorStroma richStroma dominant Composite: Stroma- Poor Rich Dominant

15. Identify differentiation UndifferentiatedPoorly differentiated Differentiating

16. MKI Calculation Low MKIIntermediate MKI High MKI

17. How to determine MKI? The number of the tumor cells in mitosis and karyorrhexis per 5000 NB cells by averaging Darker nuclei with irregular, fragmented shapes –This is how they are separated from hyperchromatic nuclei, which are more roundish uniformly dark cells (dying a silent death) Karyorrhexis cells usually have dark pinkish cytoplasm Three types –Low ( < 100 / 5000) –Intermediate( 100-200 / 5000 ) –High ( > 200 / 5000 )

18. Flowchart

19. Original Region of Interest

20. Complement of the R plane

21. Output of the Reconstruction Filter

22. Top-hat by Reconstruction

23. Hysteresis Thresholding Th Tl

24. Hysteresis Thresholding Th Tl

25. Segmented Nuclei

26. Watershed Segmentation

27. Output of Final Segmentation

28. Segmentation Example

32. Segmentation Evaluation M A

33. Experimental Results Without Hysteresis Thresholding With Hysteresis Thresholding OS1 85.76%±14.05%90.24%±5.14% OS2 91.56%±10.3994.79%±2.97%

34. Summary Feasible to do cell segmentation using morphological operations Hysteresis Thresholding improves segmentation accuracy while decreasing variability

35. Summary Application of segmentation algorithm to neuroblastoma classification –MKI calculation

36. Acknowledgment Thomas Barr, Columbus Children’s Hospital Dr. Hideki Sano, Los Angeles Children’s Hospital

37. Questions?

38. Select a ROI www.humpath.com