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 at MIT using VTK and Tcl
Lauren O’Donnell’s further development
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 NA-MIC 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; intuitive select, 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