Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching...

Surgical Planning LaboratoryBrigham and Women’s HospitalBoston, Massachusetts USA

a teaching affiliate ofHarvard Medical School

3D Slicer And The NA-MIC Kit

Ron Kikinis, M.D.

Robert Greenes Distinguished Director of Biomedical Informatics Professor of Radiology, Harvard Medical School

Founding Director, Surgical Planning Laboratory, Brigham and Women’s HospitalPrincipal Investigator, National Alliance for Medical Image Computing (a National Center for Biomedical Computing), and Neuroimage Analysis Center (a NCRR National Resource Center)Research Director, National Center for Image Guided Therapy

©2011 Surgical Planning Laboratory, ARR Slide 2

Acknowledgments

• F. Jolesz, C. Tempany, P. Black, A. Golby, S. Wells, N. Hata, CF. Westin, M. Halle, S. Pieper, F. Talos, W. Schroeder, and many more….

V E R I TAS


Our Science

• Algorithm research

• Tool development

• Biomedical Research

Provided by Odonnell, et al.Provided by Odonnell, et al.

Provided by Kindlmann, et al.Provided by Kindlmann, et al.

Golby, Archip et al.


Medical Image Computing

• Focus is on group analysis in the brain– Where is a function typically located– What is the variability?

Fischl, Greve, et al - MGH


Personalized Medicine

• Subject specific analysis

– Research in a clinical setting

– Interactive Analysis

Golby, Pieper, Lemaire, BWH Neurosurgery


A Different Style Of Research

• MIC for personalized medicine– Software runs on your computer

(HIPAA)– Fast processing (clinical research)– Subject-specific analysis (pathology)

Picture courtesy Raul San Jose


Different Communities

• Neuroimaging – Willing to spend

time: calibration of algorithm, learn to use

– Automation is very important

– Large resources are available

• Subject specific analysis– Minutes to hours

per case– Automation is

mostly not available

– Limited computational resources


Example: Image Guided Therapy (IGT)

• Use diagnostic imaging to replace/augment direct inspection

• Requires subject specific analysis of imaging data

• A very different style of research– Patient specific analysis– Limited resources are available– False positives and false negatives cannot be

compensated by adding subjects (you have only one)


Augmented Reality


The NA-MIC Approach

• Open community process

• Modular and extensible software architecture

• Free open source software (BSD)

• Works on your computer

Picture courtesy Gabor Fichtinger


This is Where We are Today

Picture courtesy Kapur, Jakab, Kikinis


NA-MIC Kit Overview


Works on Your Computer

• Easy download and installation

• Runs natively on your computer on most common operating systems behind your institutional firewall

• Training concept:

• Self guided tutorials• Training events• Registration and segmentation support

Picture courtesy Dominik Meier


The 3D Slicer (slicer.org)

• 3D Slicer is our primary platform for delivering image computing technology for personalized medicine research– Basic and clinical

visualization– Longitudinal imaging– Registration– Segmentation

Picture courtesy Marianna Jakab


Slicer 3.6 Highlights

Picture courtesy S. Pieper


Slicer 3.6 Highlights (cont)



NA-MIC Image Informatics Today

• Limited: The tradition in MIC is to chop of the header!

• Non-standard data as output from research scans

• Limited Dicom support: ITK/GDCM– Communications layer missing– Many Dicom extensions missing

• Protoype interfaces to informatics frameworks: – FetchMI in Slicer

• XNAT

• Midas


XNAT is…

• The eXtensible Neuroimaging Archive Toolkit.• Extensible, free open source software• An imaging informatics platform for managing,

processing, and sharing imaging and related data.• Developed by the Neuroinformatics Research

Group at Washington University in St. Louis.

Picture courtesy D. Marcus


Modular Open Source Architecture



QUARANTINE LOCAL USE COLLABORATION PUBLIC ACCESS

IMAGING

NON-IMAGING

OTHER SOURCES

The XNAT Enterprise workflow

• Data archiving• Quality control• Data access• Security

• Visualization• Automation• Integration• Data sharing



XNAT Web services

• RESTful web services API• Access to metadata, images, • Upload, download• Comprehensive search• Multiple formats: HTML, XML, CSV, JSON• Example clients: XNAT Gateway, PyXNAT, 3D

Slicer, Grid Wizard Engine, LONI Pipeline



client applications XNAT instance

XNAT web services

Slicer as a XNAT Client



XNAT Data Sharing Challenges

• Standards are always behind• People don’t use the standards• Lots of moving targets • Many competing entities (BIRN/ caBIG, DICOM/NIFTI,

Java/Python, SOAP/REST/DICOM)• Integration with clinical systems is a driving requirement• It’s not all about the brain



NA-MIC Kit 2012

• ITK v4:– Dicom communication layer, DCMTK– Partial GPU acceleration

• Slicer 4: Focus on usability– QT, CTK widgets– Support for layouts, hanging

protocols are coming– Improved Dicom support– Sceneviews and annotations– Image informatics support

• XNAT, other

– Extended support for plug-ins


URL’s

National Alliance for Medical ImageComputingwww.na-mic.org

Neuroimage Analysis Center Nac.spl.harvard.edu

Surgical Planning Laboratory, Brigham and Women’s Hospitalspl.harvard.edu

National Center For Image Guided Therapywww.ncigt.org

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