Dr. Farzad Khalvati – Chief Technology Officer [email protected] March 2012 Overcoming...
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Transcript of Dr. Farzad Khalvati – Chief Technology Officer [email protected] March 2012 Overcoming...
Dr. Farzad Khalvati – Chief Technology [email protected]
March 2012 www.segasist.com
Overcoming Variability in Medical Image Contouring
SegasistTM
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
Contouring is necessary
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• 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
Software A Software B Software C Software D Software E
Many modalities/cases: 664 Billion images/year in the US alone
Prostate MR Breast U/S Brain CT Prostate U/S Lung X-Ray
Extracted lesion/tissue/organ used for diagnosis/treatment planning/intervention
Contouring: The Challenge of Segmentation
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Small Problem:
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Small Problem: Contouring takes time
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Demand Snapshot: Radiation Oncology
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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)
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Volume Contouring Dose Calculation Treatment
Bigger Problem: Experts contour differently
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Contouring is qualitative….
First expert
Second expert
First expert
Second expert
Inter-Observer Variability
Biggest Problem: Same expert contours differently
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Contouring is qualitative….
First expert
First expert contours again
First expert
First expert contours again
Intra-Observer Variability
Inter- and Intra-Observer Variability
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"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
The Curse of Variability: Solution
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• 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
The Curse of Variability: Examples
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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
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It requires experts actually contour the same image
Not feasible: Too costly to afford!
Semi-Conventional Consensus Building
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• 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
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Atlas New Image
Best Match Registration
Copyright © Segasist Technologies 2008-11
Consensus Building
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Average Weighted average Distance optimization STAPLE algorithm
Segasist Reconcillio
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Variability captured
One user
Consistency verification
intra-observer variability
All users
Consensus building
inter-observer variability
Segasist Reconcillio
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ComputationalConsensus
Segasist Technologies
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• 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
www.segasist.com
SegasistTM