National Alliance for Medical Image Computing NA-MIC Work of Tannenbaum Group Computer Science and...
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National Alliance for Medical Image Computing http://www.na-mic.org NA-MIC Work of Tannenbaum Group Computer Science and Mathematics Stony Brook University
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Transcript of National Alliance for Medical Image Computing NA-MIC Work of Tannenbaum Group Computer Science and...
National Alliance for Medical Image Computing http://www.na-mic.org
NA-MIC Work of Tannenbaum Group
Computer Science and Mathematics
Stony Brook University
National Alliance for Medical Image Computing http://www.na-mic.org
In collaboration with (no particular order):
Steven HakerTauseef ur-RehmanAyelet DominitzEric PichonDelphine NainYi GaoIvan KolesovLiangJia ZhuSamuel DambrevilleJames MalcolmGanesh Sundaramoorthi
Behnood GholamiMarc NiethammerOleg Michaelovich Namrata VaswamiPeter KarasevArie NakhmaniYogesh RathiPatricio VelaVandana MohanShawn LanktonGozde Unal
Students and Postdocs
National Alliance for Medical Image Computing http://www.na-mic.org
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)
National Alliance for Medical Image Computing http://www.na-mic.org
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
National Alliance for Medical Image Computing http://www.na-mic.org
3D Interactive Segmentation
GrowCut methodEasy for user interaction
Slow for 3D images
Level sets methodFlexible to segment complex structures
Rely on good initialization
3D interactive segmentationFast GrowCut for initialization
Level sets refinement, Slicer modules e.g. KSlice
National Alliance for Medical Image Computing http://www.na-mic.org
Comparison
Method Segmentation time (seconds) Memory (MB)
Quantitative
1st edit 2nd edit 3rd edit Dice Vol. Overlap
GrowCut 210 255 269 200 97% 97%
Proposed 28 3 3 522
GrowCut:
Proposed:
Lung segmentation: image ROI [503 333 43]3 rounds of interaction/editing
National Alliance for Medical Image Computing http://www.na-mic.org
Apr 21, 20237
Particle Filtering
National Alliance for Medical Image Computing http://www.na-mic.org8
Particle Filtering
National Alliance for Medical Image Computing http://www.na-mic.org
Particle Filtering Registration
National Alliance for Medical Image Computing http://www.na-mic.org
National Alliance for Medical Image Computing http://www.na-mic.org
Longitudinal shape analysis
National Alliance for Medical Image Computing http://www.na-mic.org
Traumatic Brain Injury
National Alliance for Medical Image Computing http://www.na-mic.org
Fibrosis distribution analysis
AFib recurrence after RF ablationGroup 1, cured
Group 2, recurrence
Hypothesis: Group-wise difference between 1 and 2
Shape and fibrosis (intensity) distribution
National Alliance for Medical Image Computing http://www.na-mic.org
Results
Gray: no-statistical difference. Color region: statistically different regions.
National Alliance for Medical Image Computing http://www.na-mic.org
Hexahedral Meshes
National Alliance for Medical Image Computing http://www.na-mic.org
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)
National Alliance for Medical Image Computing http://www.na-mic.org
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.
