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Advanced Medical Image Segmentation and Analysis Collaborative Research

Explore cutting-edge research on segmentation, shape theory, registration, and meshing in medical image analysis. Includes interactive segmentation tools and ongoing projects in lung segmentation, fibrosis analysis, and shape modeling. Collaborations with experts in the field. Future work in tumor margin delineation, neurosurgery validation, and microanatomical imaging. Conclude with acknowledgments and words of gratitude to collaborators. May the Force be with you and Slicer!

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Advanced Medical Image Segmentation and Analysis Collaborative Research

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  1. NA-MIC Work of Tannenbaum Group Computer Science and Mathematics Stony Brook University

  2. Students and Postdocs In collaboration with (no particular order): Steven Haker Tauseef ur-Rehman Ayelet Dominitz Eric Pichon Delphine Nain Yi Gao Ivan Kolesov LiangJia Zhu Samuel Dambreville James Malcolm Ganesh Sundaramoorthi BehnoodGholami Marc Niethammer Oleg Michaelovich NamrataVaswami Peter Karasev ArieNakhmani YogeshRathi Patricio Vela Vandana Mohan Shawn Lankton GozdeUnal

  3. Assorted Projects • Segmentation: Local/Global, Sobolev, Finsler, Steerable, Optimal Control • Shape Theory: Spherical Wavelets, OMT • Registration: OMT, Particle Filtering, Optimal Control • Meshing (hexahedral) • Conformal maps (brain warping, colon fly-throughs)

  4. KSlice Interactive Segmentation Added Features: • Editor module • Inter-slice interpolation • Control of user input function • Choice for image cost functional • Selection of tools for input

  5. 3D Interactive Segmentation • GrowCut method • Easy for user interaction • Slow for 3D images • Level sets method • Flexible to segment complex structures • Rely on good initialization • 3D interactive segmentation • Fast GrowCut for initialization • Level sets refinement, Slicer modules e.g. KSlice

  6. Comparison • Lung segmentation: image ROI [503 333 43] • 3 rounds of interaction/editing GrowCut: Proposed:

  7. Particle Filtering

  8. Particle Filtering

  9. Particle Filtering Registration

  10. Longitudinal shape analysis

  11. Traumatic Brain Injury

  12. Fibrosis distribution analysis • AFib recurrence after RF ablation • Group 1, cured • Group 2, recurrence • Hypothesis: • Group-wise difference between 1 and 2 • Shape and fibrosis (intensity) distribution

  13. Results Gray: no-statistical difference. Color region: statistically different regions.

  14. Hexahedral Meshes

  15. Future Work • Compressive Sensing/Mass Spec/Raman Spectroscopy for better tumor margin delineation (Nathalie Agar, Alex Golby, Yi Gao) • DECS for neurosurgery/validation (Sonia Pujols, Yi Gao) • Microanatomical imaging (Joel Saltz) • Radiation oncology (Harini V., Joe Deasy, Greg Sharp, Ivan Kolesov, Yi Gao) • Fibrosis analysis (Rob MacLeod, Josh Cates, Yi Gao, LiangJia Zhu)

  16. Conclusions • Thank you to all the collaborators and especially to Ron Kikinis for giving us this great opportunity! • May the Force be with you and Slicer.

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