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Image Processing with Slicer

Image Processing with Slicer. Chand T. John Neuromuscular Biomechanics Lab Computer Science Department. Biomedical Image Consumers. Driving Biomedical Fields. Neuromuscular Biomechanics. Brain Imaging. Virtual Endoscopy. Molecular Dynamics. Biomedical Imaging.

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Image Processing with Slicer

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  1. Image Processing with Slicer Chand T. John Neuromuscular Biomechanics Lab Computer Science Department

  2. Biomedical Image Consumers Driving Biomedical Fields Neuromuscular Biomechanics Brain Imaging Virtual Endoscopy Molecular Dynamics Biomedical Imaging

  3. Biomedical Image Processing • Different fields, similar general needs • Different fields, different specific needs • Currently: context-specific solutions only • Where’s the good, cheap, non-pirated software?! • Bold claim: a single extensible, open-source application can satisfy the image processing needs of all fields

  4. A Solution Was Born • SPL (Harvard) Image Guided Surgery and Visualization • Slicer pulled together by David Gering 1997-1999 at MIT using VTK and Tcl • Lauren O’Donnell’s further development 1999-2001 • Ongoing development of Slicer’s Base by Steve Pieper and Nicole Aucoin • Individual developers inject their modules

  5. What Slicer Does • Registration (manual, automatic) • Segmentation (manual, automatic) • Model Construction • Visualization • Measurements • Individual modules…

  6. Simbios and NAMIC • Simbios • Biomedical simulation • Modeling of muscles, blood, RNA, myosin • NA-MIC • Biomedical modeling, image processing • Modeling of brain tissue, some other organs

  7. NMBL-SPL Similarities • Need good general segmentation tools • Automatic • Manual • Need accurate, fast model building tools for clinical non-programmer end users • Need software to manipulate models: modification, registration, Boolean operations

  8. NMBL-SPL Differences • Conventional segmentation practices • NMBL: All manual • SPL: Some automatic, some manual • Conventional segmentation detail level • NMBL: Subpixel accuracy • SPL: Pixel-level accuracy • Modeling pipeline • NMBL: Points, cardinal spline, samples, model via 3D Delaunay-based lofting • SPL: Points, marching cube, decimation, smoothing

  9. What did Chand Do, and Why? • Slicer developers focused mostly on automated segmentation tools • Thresholding • Mathematical morphological operators • Level set segmentation • As a result, the image editor was largely untouched since David Gering’s initial version • Clinical researchers want that extra level of manual control (e.g. NMBL) • I developed more sophisticated manual segmentation tools based on users’ needs and existing commercial software

  10. Slicer Architecture • VTK (C++) wrapped in Tcl/Tk • Create, control C++ objects in Tcl code • Do most computation in C++ code • Debugging can be a pain

  11. Slicer Demo! • Load Dicom knee data, 6-slices only • Manual segmentation of cartilage • Draw; intuitiveselect, move, insert* • Sample density*, cardinal splines* • Clear before apply*, delete after apply • Unapply* and edit operations* • Model construction (note SimTK export*, greater compatibility, reusability) • Visualization: turn off backface culling * My contributions

  12. Neuromusculoskeletal…gezundheit • Musculoskeletal modeling • More accurate biomechanical models of muscle, tendon, bone, cartilage • Current pipeline mixes (too?) many shape representations • Neuromusculoskeletal simulation • Conventionally: dynamic optimization, slow • New methods: computed muscle control

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