1. Open Source Radiology
David Kelton, MD
Feb 13, 2006

2. Outline My interest in OS Research Projects: 1) Survey OS in radiology
2) OS CAD at U of T

3. Background Interest
“Thank goodness I’m a journalist and not a radiologist”
Friedman, Thomas L. The World is Flat. New York: Farrar, Straus and Giroux

4. Overview of Imaging Infrastructure

5. Part 1: OS Opportunities in Rads
Opportunities for OS in radiology:
Ideal for standards
DICOM (Digital Imaging and Communication in Medicine)
Standard for handling, storing, and transmitting information
File format definition and a network communications protocol
IHE (Integrating the Healthcare Experience)
Ideal for research tools
Image analysis
Teaching tools

6. Part1: Survey of OS Rads Community
Dynamic sources
Sourceforge.net
Openrad.com
DICOM SERVER:
CTN
DCMTK
dcm4che
Conquest
MIND
Dicomparser
Dcmrouter
Dicom3tools
JDICOM
Medwx
MESA
DICOM Viewer
DicomWorks
DICOMscope
EViewBox
AccuLite
ezDICOM
FP Image
ImageJ
Imread
Irfanview32
Jivex
MRIcro
OSIRIS
Sante Viewer
Simple DICOM
TomoVision
XnView
Amide
NIH Image
Scion Image
OsiriX

7. Part 1: Survey of OS Rads Community
Full PACS
DIOWave
CDIMEDIC
Miniwebpacs
Teaching Files
myPACS.net
MIRC
SimpleMIRC
Image Processing
VTK
ITK
FSL
AFNI
SPM
NeatMed
ImageMagick
XMedCon

8. Part 1: Barriers to OS in Radiology
Barriers Identified to OS in Radiology:
Erickson, Langer, Nagy. The Role of Open-Source Software in Innovation and Standardization in Radiology. JACR 2005;2:
1) “White Hat”
SCAR Conference (Project OS)
2) Active Community
IT knowledge in physicians
3) “Red Hat”
MedSphere
Aycan

9. Part 1: Leading OS Rads Projects
OsiriX viewer
Increasing funding by Apple
8000+ users
An emphasis on visual navigation (3D/4D/5D)

10. Part 1: Case Study OS in Radiology
Beaumont Hospital in Ireland
Est €13 million over 5 years
Radiology specific:
€ 250,000 for OS RIS set-up
Equivalent hospital in Ireland spent € 4.3 million commercial PACS
Fitzgerald, B. Kenny, T. Lessons from a Large-Scale OSS Implementation. Twenty-fourth International Conference on Information Systems. Seattle,W ashington

11. OS Radiology in Action

12. Part 2: OS CAD
Computer-aided detection (CAD) is an emerging area of research and innovation in medical imaging to help cope with the huge increases in imaging data
Number of CAD papers at RSNA (annual radiology meeting)
2000 – 55
2001 – 86
2002 – 134
2003 – 191
2004 – 244
2005 – 281
Basic concepts:
Computer output as a second opinion
Improve accuracy and consistency of radiological interpretation, especially in screening populations (breast, lung, colon cancers)
Reduce reading time

13. Part 2: OS CAD Commercial History:
FDA approval for mammography in 1998
In USA, >1500 CAD systems for breast cancer
FDA approval for lung cancer in 2001
Under review:
Colon cancer
Renal cancer
Liver cancer
Cardiovascular disease

14. Part 2: OS CAD Is there a role for OS CAD? Basic technologies in CAD:
Doi, K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol ; 78 Spec No 1: S3-S19.
1) image processing for detection and extraction of abnormalities
2) quantitation of image features for candidates of abnormalities
3) data processing for classification of image features between normals and abnormals
4) quantitative evaluation and retrieval of images similar to those of uknown lesions
5) observer performance using ROC analysis
- Studies show a gain of 20% in early detection of breast cancers using CAD (controversial)

15. Part 2: OS CAD OS CAD tools:
1) image processing for detection and extraction of abnormalities
2) quantitation of image features for candidates of abnormalities
ImageJ (Java)
ITK/VTK (C++, funded $10 million by NLM)
NeatVision (Java)

16. Part 2: OS CAD at U of T Where is the need?
Reviewing Doi’s CAD technology components, there are two clear gaps:
1) Observer performance, or clinical validation
2) Normal/abnormal datasets to build/test these algorithms

17. Part 2: OS CAD at U of T Whitepaper Proposal:
1) Establish a CAD clinical testing network at U of T where OS algorithms can be clinically evaluated. Proposal is to refine the process utilizing our strengths (lung cancer CAD expertise)
2) Outline barriers (HIPAA, cost) to creating open database of image datasets with corresponding reports

18. Acknowledgements:
Supervisor, Dr. Haider
Project OS Committee