Dr. Farzad Khalvati – Chief Technology Officer March 2012 Overcoming Variability in Medical Image Contouring.

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Presentation transcript:

Dr. Farzad Khalvati – Chief Technology Officer March Overcoming Variability in Medical Image Contouring Segasist TM

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 ebo-enterprises.com

Contouring is necessary 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

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

Small Problem: Copyright © Segasist Technologies

Small Problem: Contouring takes time Copyright © Segasist Technologies

Demand Snapshot: Radiation Oncology Copyright © Segasist Technologies : 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 ( hours/patient) 7 Volume Contouring Dose Calculation Treatment

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

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

Inter- and Intra-Observer Variability "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

The Curse of Variability: Solution 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

The Curse of Variability: Examples Copyright © Segasist Technologies  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]  …

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

Semi-Conventional Consensus Building Copyright © Segasist Technologies 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

Conventional Atlas-Based Segmentation Atlas New Image Best Match Registration Copyright © Segasist Technologies

Consensus Building Copyright © Segasist Technologies  Average  Weighted average  Distance optimization  STAPLE algorithm

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

Segasist Reconcillio Copyright © Segasist Technologies Computational Consensus

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

Thank You Questions? Dr. Farzad Khalvati, Ph.D. – Chief Technology Officer Segasist TM