ITK Tutorial Presentation Slides-953
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3D Slicer is an open-source software platform for medical image computing and visualization that allows users to load, segment, register, and analyze medical images from various modalities like MRI, CT, and DICOM. It provides tools for tasks such as interactive image segmentation, registration, visualization of diffusion tensor images and tractography, and integration of functional and anatomical data. Slicer has been used in applications such as surgical planning, virtual endoscopy, and pre-operative mapping of functional areas to avoid during surgery.
ITK Tutorial Presentation Slides-953
- 1. Slicer Overview Steve Pieper, PhD
- 2. Outline Slicer Overview and History Architecture / Implementation Image I/O and Management Segmentation Tools (EM, Editor) DTMRI Tools fMRI Tools Applications (IGT, Clinical Research)
- 3. What is 3D Slicer? 3D Slicer is… An end-user application for 3D medical image computing research and Image Guided Therapy A platform for research where new techniques can be plugged into a useful framework A freely-downloadable program with source and binaries for Windows, Linux, Solaris and (sort-of) Mac OSX NOT an FDA approved medical device and CANNOT be used clinically without proper research controls (IRB etc.) NOT finished – some parts will work better than others
- 4. Surgical Planning Example Dr. Jose Miguel Selman, Clinica Las Condes, Santiago, CHILE Temporal Lobe Cavernoma MR Cortex and White Matter Extracted by FreeSurfer (MGH software, interface created for BIRN) Registration to CT and Visualization in Slicer
- 5. Virtual Endoscopy Example Delphine Nain, MIT AI Lab, now at Georgia Tech Automatic or Manual Path Planning Animated Camera and Controls http://www.ai.mit.edu/projects/medical-vision/virtual-endoscopy/
- 6. Slicer Background SPL Image Guided Surgery and Visualization (Kikinis, Westin, Hata, Halle, others) Slicer Application Pulled Together by Dave Gering 1997-1999 with VTK and Tcl Further Development and Architecture by Lauren O’Donnell 1999-2001 Ongoing Development of Slicer’s Base Primarily by Steve Pieper and Nicole Aucoin Many Modules and Contributions by Various Authors BWH, MIT, MGH, Georgia Tech, UCSD, JHU…
- 7. Why Develop with Slicer? Start with a Powerful Platform Remove Obstacles to Problem Solving Access to Every Layer of Source Code: Numeric, Graphic, Network, etc. Commit to an Environment that will Always be Available Not Tied Up in IP of Old Institution Not Tied to Proprietary Platform with License Fees Keep Your Own Work Available to You
- 8. Slicer Today 300K Lines of Code Cross-Platform Tcl/Tk GUI VTK/ITK Based C++ Computing www.slicer.org 166 on slicer-users 117 on slicer-devel 4000 Registered Downloads
- 9. Architecture Plug-in Modules consist of Tcl and C++ code with cross-platform file layout for building and loading Standard naming convention and hooks to add GUI and processing components Personally, I find the speed of C++ and the interactive development of Tcl to be a near-perfect environment 3D Slicer Application Slicer Base Module 1 Module N … VTK Tcl OpenGL Window System Computer Hardware
- 10. Features Load Medical Image Data: MR, CT in DICOM, GE, Analyze… XML-based File Format: MRML (Medical Reality Markup Language) Interactive Editor: Draw, Threshold, Math Morphology… Automated Segmenters: EM Segmentation, Fast Marching, Level Sets… Visualization: Model Building, Stereo Rendering, Animation… Registration: Manual, ITK, CNI Measurement: Fiducial-Based, Volumetric, Polyhedral Intersection, Vessel Cross-Section, Osteotomy Planning IGT: Tracked Probes, Real-Time Images, Robot Control Additional Application-Specific Features in Modules…
- 11. Administration Project Housed at Surgical Planning Lab, Brigham and Women’s Hospital / Harvard Medical School, Many MIT CSAIL (formerly AI Lab) Students and Faculty Involved CVS, Mailing Lists, etc at the SPL Funded Projects Supporting Slicer Base Development Neuroimage Analysis Center (NAC): NIH Center at SPL Biomedical Informatics Research Network (BIRN): NIH Supported Neuroimaging Collaboration Computer Integrated Surgical Systems and Technology (CISST): NSF Supported Robotics Collaboration Headed by JHU Virtual Soldier: DARPA Organ Simulation Collaboration National Alliance for Medical Image Processing Module Development Supported by Application-Oriented Grants
- 12. Image/Scene Management XML-Based MRML File Stores Scene Description Volumes (Images, Label Maps) Models Hierarchical Affine Transforms Scene Data (Cameras, Colors, Fiducials, etc). Manipulated in World Coordinates based on Patient RAS Automatically Extracted from DICOM or GE Files
- 13. Image Formats DICOM, GE, Headerless, Analyze Best Support for MR CT and RGB Support limited Real-Time from OpenMR and US Time Series Analyze Sequence DICOM Sequence BXH Files BIAC XML Header (Brian Image Analysis Center, Duke) Like MRML for fMRI; Integration Work Ongoing
- 14. User Segmentation Tools Label Map Editor Draw on Orthogonal Planes Connected Component “Island” Tools Math Morphology Image Masking and Logical Operations Level Set, Fast Marching
- 15. EM Segmenter Hierarchical segmentation based on anatomy Multi threaded User friendly interface * For further information see Pohl et al. “Incorporating Non-Rigid Registration into Expectation Maximization Algorithm to Segment MR Images”, MICCAI 2002, pp. 564-572 Segmentation tool designed for fully automatic, high-quality parcellation of the brain*: Segmentation of cortical and ventricle substructures Multi channel input
- 16. Models Triangle Meshes from Label Maps Marching Cubes, Decimation, Smoothing Model Hierarchies Clipping By Slice Planes Image: Developmental Neuroinformatics, Simon Warfield
- 17. Registration Interactive Manual Transform Editing Landmark Based Alignment Rigid Intensity Registration Mutual Information Metric ITK Implementation Non-Rigid Registration Demon’s Method Available by Request to CNI
- 18. Diffusion Tensor Tractography Multiple MR Gradient Acquisitions Sensitive to Brownian Diffusion of Water Cell Membranes Restrict Diffusion Post Processing to Extract Probable White Matter Tracts Actual Tracts are Far Below the Resolution of the Scan Images Provided by Westin, Park, O’Donnell et al
- 19. DTMRI Tools Convert Gradient Images to Tensors Generate Scalars ADC, FA, etc Visualize Glyphs Tractography User Guided From ROI
- 20. Segmentation and Tractography Parcellation Freesurfer (MGH) Tractography DoDTI (H.J. Park) Visualization Slicer Full Integration with Slicer Planned
- 21. DTMRI Examples White Matter Atlas Under Development in Slicer by James Fallon, UCI
- 22. Slicer fMRI Goals Advanced GUI and Interactive Visualization Environment iBrowser Platform for Activation Detector Research fMRIEngine Framework for fMRI Integration with Other Modalities
- 23. Application: Pre-Operative Map Structural MRI Tumor Segmentation DTI Diffusion Tensor Imaging fMRI Functional MRI MEG Magneto Encephlogram Anatomy Atlas “ Textbook” Information