Segasist
TM
Overcoming Variability in Medical
Image Contouring
Dr. Farzad Khalvati – Chief Technology Officer
farzad.khalvati@segasist.com
March 2012
www.segasist.com
ebo-enterprises.com
Contouring
Region of interest (ROI), e.g. tumour
Contouring by clinician
(radiologist, oncologist,
pathologist etc.)
Any medical image
(CT/MRI/US/PET etc.)
Copyright © Segasist Technologies 2008-11
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
-3Copyright © Segasist Technologies 2008-11
Contouring: The Challenge of Segmentation
Many modalities/cases: 664 Billion images/year in the US alone
Prostate MR
Breast U/S
Brain CT
Prostate U/S
Lung X-Ray
Software A
Software B
Software C
Software D
Software E
Extracted lesion/tissue/organ used for
diagnosis/treatment planning/intervention
-4Copyright © Segasist Technologies 2008-11
Small Problem:
-5Copyright © Segasist Technologies 2008-11
Small Problem: Contouring takes time
-6Copyright © Segasist Technologies 2008-11
Demand Snapshot: Radiation Oncology
Volume Contouring
Dose Calculation
Treatment
Copyright © Segasist Technologies 2009-11
Contouring is a major bottleneck
(0.25-3 hours/patient)
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
7
Bigger Problem: Experts contour differently
Contouring is
qualitative….
First expert
Second expert
Inter-Observer Variability
First expert
Second expert
-8Copyright © Segasist Technologies 2008-11
Biggest Problem: Same expert contours differently
Contouring is
qualitative….
First expert
First expert contours
again
Intra-Observer Variability
First expert
First expert contours
again
-9Copyright © Segasist Technologies 2008-11
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
- 10 Copyright © Segasist Technologies 2008-11
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
- 11 Copyright © Segasist Technologies 2008-11
The Curse of Variability: Examples
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]
…
- 12 Copyright © Segasist Technologies 2008-11
Conventional Consensus Building
It requires experts actually contour the same image
Not feasible:
Too costly to
afford!
- 13 Copyright © Segasist Technologies 2008-11
Semi-Conventional Consensus Building
• 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
- 14 Copyright © Segasist Technologies 2008-11
Conventional Atlas-Based Segmentation
Atlas
New Image
Best Match
- 15 Copyright © Segasist Technologies 2008-11
Registration
Consensus Building
 Average
 Weighted average
 Distance optimization
 STAPLE algorithm
- 16 Copyright © Segasist Technologies 2008-11
Segasist Reconcillio
Variability captured
One user
All users
Consistency
verification
Consensus
building
intra-observer
variability
inter-observer
variability
- 17 -
Copyright © Segasist Technologies 2008-11
Segasist Reconcillio
Computational
Consensus
- 18 Copyright © Segasist Technologies 2008-11
Segasist Technologies
• University of Waterloo Spin-off
• Founded in 2008
• Toronto-based
• Products:
• Prostate Auto-Contouring: FDA cleared
• Segasist Auto-Contouring
• Segasist Reconcillio
- 19 Copyright © Segasist Technologies 2008-11
Segasist
TM
Thank You
Questions?
Dr. Farzad Khalvati, Ph.D. – Chief Technology Officer
farzad.khalvati@segasist.com
www.segasist.com