Open Source RadiologyOpen Source Radiology

David Kelton, MDDavid Kelton, MD

Feb 13, 2006Feb 13, 2006

OutlineOutline

My interest in OSMy interest in OS Research Projects:Research Projects:

1) Survey OS in radiology1) Survey OS in radiology 2) OS CAD at U of T2) OS CAD at U of T

Background InterestBackground Interest

““Thank goodness I’m a journalist and Thank goodness I’m a journalist and not a radiologist”not a radiologist”

Friedman, Thomas L. Friedman, Thomas L. The World is FlatThe World is Flat. . New York: Farrar, Straus and Giroux 2005.New York: Farrar, Straus and Giroux 2005.

Overview of Imaging Overview of Imaging InfrastructureInfrastructure

Part 1: OS Opportunities in Part 1: OS Opportunities in RadsRads

Opportunities for OS in radiology:Opportunities for OS in radiology: Ideal for standardsIdeal for standards

DICOM (Digital Imaging and Communication in DICOM (Digital Imaging and Communication in Medicine)Medicine)

Standard for handling, storing, and transmitting Standard for handling, storing, and transmitting informationinformation

File format definition and a network communications File format definition and a network communications protocolprotocol

IHE (Integrating the Healthcare Experience)IHE (Integrating the Healthcare Experience) Ideal for research toolsIdeal for research tools

Image analysisImage analysis Teaching toolsTeaching tools

Part1: Survey of OS Rads Part1: Survey of OS Rads CommunityCommunity

Dynamic sourcesDynamic sources Sourceforge.netSourceforge.net Openrad.comOpenrad.com

DICOM SERVER:DICOM SERVER: CTNCTN DCMTKDCMTK dcm4chedcm4che ConquestConquest MINDMIND DicomparserDicomparser DcmrouterDcmrouter Dicom3toolsDicom3tools JDICOMJDICOM MedwxMedwx MESAMESA

DICOM ViewerDICOM Viewer DicomWorksDicomWorks DICOMscopeDICOMscope EViewBoxEViewBox AccuLiteAccuLite ezDICOMezDICOM FP ImageFP Image ImageJImageJ ImreadImread Irfanview32Irfanview32 JivexJivex MRIcroMRIcro OSIRISOSIRIS Sante ViewerSante Viewer Simple DICOMSimple DICOM TomoVisionTomoVision XnViewXnView AmideAmide NIH ImageNIH Image Scion ImageScion Image OsiriXOsiriX

Part 1: Survey of OS Rads Part 1: Survey of OS Rads CommunityCommunity

Full PACSFull PACS DIOWaveDIOWave CDIMEDICCDIMEDIC MiniwebpacsMiniwebpacs

Teaching FilesTeaching Files myPACS.netmyPACS.net MIRCMIRC SimpleMIRCSimpleMIRC

Image ProcessingImage Processing VTKVTK ITKITK FSLFSL AFNIAFNI SPMSPM NeatMedNeatMed ImageMagickImageMagick XMedConXMedCon

Part 1: Barriers to OS in Part 1: Barriers to OS in RadiologyRadiology

Barriers Identified to OS in Radiology:Barriers Identified to OS in Radiology: Erickson, Langer, Nagy. The Role of Open-Source Software in Erickson, Langer, Nagy. The Role of Open-Source Software in

Innovation and Standardization in Radiology. JACR 2005;2: 927-Innovation and Standardization in Radiology. JACR 2005;2: 927-931.931.

1) “White Hat”1) “White Hat” SCAR Conference (Project OS)SCAR Conference (Project OS)

2) Active Community2) Active Community IT knowledge in physiciansIT knowledge in physicians

3) “Red Hat”3) “Red Hat” MedSphereMedSphere AycanAycan

Part 1: Leading OS Rads Part 1: Leading OS Rads ProjectsProjects

OsiriX viewerOsiriX viewer Increasing funding by AppleIncreasing funding by Apple 8000+ users8000+ users An emphasis on visual An emphasis on visual

navigation (3D/4D/5D)navigation (3D/4D/5D)

Part 1: Case Study OS in Part 1: Case Study OS in RadiologyRadiology

Case studyCase study Beaumont Hospital in Beaumont Hospital in

IrelandIreland Est Est €13 million over 5 €13 million over 5

yearsyears Radiology specific:Radiology specific:

€ € 250,000 for OS RIS 250,000 for OS RIS set-upset-up

Equivalent hospital in Equivalent hospital in Ireland spent € 4.3 Ireland spent € 4.3 million commercial million commercial PACSPACS

Fitzgerald, B. Kenny, T. Lessons from a Large-Fitzgerald, B. Kenny, T. Lessons from a Large-Scale OSS Implementation. Twenty-fourth Scale OSS Implementation. Twenty-fourth International Conference on Information Systems. International Conference on Information Systems. Seattle,W ashington. 2003. Seattle,W ashington. 2003.

OS Radiology in ActionOS Radiology in Action

Part 2: OS CADPart 2: OS CAD

Computer-aided detection (CAD) is an emerging area of Computer-aided detection (CAD) is an emerging area of research and innovation in medical imaging to help cope research and innovation in medical imaging to help cope with the huge increases in imaging datawith the huge increases in imaging data Number of CAD papers at RSNA (annual radiology meeting)Number of CAD papers at RSNA (annual radiology meeting)

2000 – 552000 – 55 2001 – 862001 – 86 2002 – 1342002 – 134 2003 – 1912003 – 191 2004 – 2442004 – 244 2005 – 2812005 – 281

Basic concepts:Basic concepts: Computer output as a second opinionComputer output as a second opinion Improve accuracy and consistency of radiological Improve accuracy and consistency of radiological

interpretation, especially in screening populations (breast, interpretation, especially in screening populations (breast, lung, colon cancers)lung, colon cancers)

Reduce reading timeReduce reading time

Part 2: OS CADPart 2: OS CAD

Commercial History:Commercial History: FDA approval for mammography in 1998FDA approval for mammography in 1998 In USA, >1500 CAD systems for breast In USA, >1500 CAD systems for breast

cancercancer FDA approval for lung cancer in 2001FDA approval for lung cancer in 2001 Under review:Under review:

Colon cancerColon cancer Renal cancerRenal cancer Liver cancerLiver cancer Cardiovascular diseaseCardiovascular disease

Part 2: OS CADPart 2: OS CAD

Is there a role for OS CAD?Is there a role for OS CAD? Basic technologies in CAD:Basic technologies in CAD:

Doi, K. Current status and future potential of computer-aided diagnosis in medical Doi, K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol. 2005; 78 Spec No 1: S3-S19.imaging. Br J Radiol. 2005; 78 Spec No 1: S3-S19.

1) image processing for detection and extraction of 1) image processing for detection and extraction of abnormalitiesabnormalities

2) quantitation of image features for candidates of 2) quantitation of image features for candidates of abnormalitiesabnormalities

3) data processing for classification of image features 3) data processing for classification of image features between normals and abnormalsbetween normals and abnormals

4) quantitative evaluation and retrieval of images similar to 4) quantitative evaluation and retrieval of images similar to those of uknown lesionsthose of uknown lesions

5) observer performance using ROC analysis5) observer performance using ROC analysis

- Studies show a gain of 20% in early detection of breast - Studies show a gain of 20% in early detection of breast cancers using CAD (controversial)cancers using CAD (controversial)

Part 2: OS CADPart 2: OS CAD

OS CAD tools:OS CAD tools: 1) image processing for detection and 1) image processing for detection and

extraction of abnormalitiesextraction of abnormalities 2) quantitation of image features for 2) quantitation of image features for

candidates of abnormalitiescandidates of abnormalities ImageJ (Java)ImageJ (Java) ITK/VTK (C++, funded $10 million by NLM)ITK/VTK (C++, funded $10 million by NLM) NeatVision (Java)NeatVision (Java)

Part 2: OS CAD at U of TPart 2: OS CAD at U of T

Where is the need?Where is the need? Reviewing Doi’s CAD technology Reviewing Doi’s CAD technology

components, there are two clear gaps:components, there are two clear gaps: 1) Observer performance, or clinical 1) Observer performance, or clinical

validationvalidation 2) Normal/abnormal datasets to build/test 2) Normal/abnormal datasets to build/test

these algorithmsthese algorithms

Part 2: OS CAD at U of TPart 2: OS CAD at U of T

Whitepaper Proposal:Whitepaper Proposal: 1) Establish a CAD clinical testing 1) Establish a CAD clinical testing

network at U of T where OS algorithms network at U of T where OS algorithms can be clinically evaluated. Proposal is can be clinically evaluated. Proposal is to refine the process utilizing our to refine the process utilizing our strengths (lung cancer CAD expertise)strengths (lung cancer CAD expertise)

2) Outline barriers (HIPAA, cost) to 2) Outline barriers (HIPAA, cost) to creating open database of image creating open database of image datasets with corresponding reports datasets with corresponding reports

Acknowledgements:Acknowledgements:

Supervisor, Dr. HaiderSupervisor, Dr. Haider Project OS CommitteeProject OS Committee