Liver Segmentation Using Active Learning

1. Liver Segmentation Using Active Learning Ankur Bakshi Allison Petrosino Advisor: Dr. Jacob Furst August 21, 2008

2. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

3. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

4. Introduction • Liver has many important functions • Liver cancer is 4th most common malignancy in the world • Computed Tomography (CT) scans are a common tool for diagnosis

5. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

6. Problem Statement • Liver Segmentation is an important first step for Computer-Aided Diagnosis (CAD) • Difficulties associated with liver segmentation • Time consuming • Similarities to other organs Source: Comparison and Evaluation of Methods for Liver Segmentation from CT datasets, Heimann et al., 2008

7. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

8. Related Work • Heimann et al.- statistical shape based segmentation • Susomboon et al.- hybrid liver segmentation • Tur et al.- natural language application • Tong et al.- text classification • Turtinen et al.- texture application • Prasad et al.- emphysema classification

9. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

10. Liver Segmentation Algorithm

11. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

12. Methods Explored • Passive Learning • Active Learning • 1000 vs 100 initial examples • 100 vs 10 examples added • Negatives taken from evaluated non-liver vs. all non-liver • Most informative vs Hierarchical • Gabor

13. Hierarchical Method

14. Post-Processing

15. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

16. Results, Patient 1

17. Results, Patient 1

18. Results, Patient 1 Slice 134 Slice 135 Slice 136 Slice 137 Slice 138 Slice 139

19. Results, Patient 3

20. Results, Patient 20

21. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions

22. Conclusion • Classifier based approach outperforms confidence interval based approach • Active learning outperforms passive learning • Different active learning methods have similar results • 10 examples, evaluated non-liver is most promising • Interesting structures highlighted for application in CADx systems

23. Agenda • Introduction • Problem Statement • Related Work • Liver Segmentation • Methods • Results • Conclusion • Questions