1. Dr. Farzad Khalvati – Chief Technology Officer farzad.khalvati@segasist.com March 2012 www.segasist.com Overcoming Variability in Medical Image Contouring Segasist TM

2. Contouring Any medical image (CT/MRI/US/PET etc.) Region of interest (ROI), e.g. tumour Contouring by clinician (radiologist, oncologist, pathologist etc.) Copyright © Segasist Technologies 2008-11 ebo-enterprises.com

3. Contouring is necessary - 3 - Cancer treatment needs contouring Cancer occurs frequently; e.g. Prostate cancer: The most common non-skin cancer for adult males The third leading cause of cancer death for men in Canada with incidence rates on the rise One in six men in Canada will be afflicted by prostate cancer during their lifetimes. Contouring is an important part of diagnosis, monitoring, and treatment Copyright © Segasist Technologies 2008-11

4. Software ASoftware BSoftware CSoftware DSoftware E Many modalities/cases: 664 Billion images/year in the US alone Prostate MRBreast U/SBrain CTProstate U/SLung X-Ray Extracted lesion/tissue/organ used for diagnosis/treatment planning/intervention Contouring: The Challenge of Segmentation Copyright © Segasist Technologies 2008-11 - 4 -

5. Small Problem: - 5 - Copyright © Segasist Technologies 2008-11

6. Small Problem: Contouring takes time - 6 - Copyright © Segasist Technologies 2008-11

7. Demand Snapshot: Radiation Oncology Copyright © Segasist Technologies 2009-11 2010-2020: The number of cancer patients will increase by 22%, while the number of radiation oncologists will increase by just 2%. Study published in The Journal of Clinical Oncology, October 18, 2010 Contouring is a major bottleneck (0.25-3 hours/patient) 7 Volume Contouring Dose Calculation Treatment

8. Bigger Problem: Experts contour differently - 8 - Copyright © Segasist Technologies 2008-11 Contouring is qualitative…. First expert Second expert First expert Second expert Inter-Observer Variability

9. Biggest Problem: Same expert contours differently - 9 - Copyright © Segasist Technologies 2008-11 Contouring is qualitative …. First expert First expert contours again First expert First expert contours again Intra-Observer Variability

10. Inter- and Intra-Observer Variability - 10 - "The failure by the observer to measure or identify a phenomenon accurately, which results in an error. Sources for this may be due to the observer's missing an abnormality, or to faulty technique resulting in incorrect test measurement, or to misinterpretation of the data." Source: National Library of Medicine  Inherent anatomical vagueness/ambiguity  Limitations of imaging devices  Level of expertise of the expert  (Partial) Subjectivity Copyright © Segasist Technologies 2008-11

11. The Curse of Variability: Solution - 11 - There is no Perfect segmentation algorithm Consensus Contour: for a given organ/tumour, consensus contour of multiple contours is the one that agrees with all of them the most Different algorithms can be used: STAPLE The result contour has maximum sensitivity and specificity with all input contours Copyright © Segasist Technologies 2008-11

12. The Curse of Variability: Examples Copyright © Segasist Technologies 2008-11 - 12 -  Soft-tissue sarcoma: 13% [Roberge et al., Cancer/Radiothérapie 2011]  Prostate: 18% [White et al., Clinical Oncology 2009]  Bladder: 32% [Foroudi et al., Med. Imaging & Rad. Onc., 2009]  Abdominal aorta: 40% [England et al., Radiography 2008]  Breast lumpectomy cavity: 45% [Dzhugashvili et al., Rad.Onc. 2009]  Pulmonary nodules: 54% [Bogot et al., Academic Radiology 2005]  …

13. Conventional Consensus Building - 13 - Copyright © Segasist Technologies 2008-11 It requires experts actually contour the same image Not feasible: Too costly to afford!

14. Semi-Conventional Consensus Building - 14 - Copyright © Segasist Technologies 2008-11 Instead of experts actually contour the same image; Use previously created Atlases of the experts to generate contours Use the Atlas-based generated contours to build consensus

15. Conventional Atlas-Based Segmentation - 15 - Atlas New Image Best Match Registration Copyright © Segasist Technologies 2008-11

16. Consensus Building - 16 - Copyright © Segasist Technologies 2008-11  Average  Weighted average  Distance optimization  STAPLE algorithm

17. Segasist Reconcillio - 17 - Copyright © Segasist Technologies 2008-11 Variability captured One user Consistency verification intra-observer variability All users Consensus building inter-observer variability

18. Segasist Reconcillio - 18 - Copyright © Segasist Technologies 2008-11 Computational Consensus

19. Segasist Technologies - 19 - Copyright © Segasist Technologies 2008-11 University of Waterloo Spin-off Founded in 2008 Toronto-based Products: Prostate Auto-Contouring: FDA cleared Segasist Auto-Contouring Segasist Reconcillio

20. Thank You Questions? Dr. Farzad Khalvati, Ph.D. – Chief Technology Officer farzad.khalvati@segasist.com www.segasist.com Segasist TM