KSlice Interactive Segmentation - National Alliance for Medical

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NA-MIC Work of Tannenbaum Group
Computer Science and Mathematics
Stony Brook University
National Alliance for Medical Image Computing
http://www.na-mic.org
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
National Alliance for Medical Image Computing
http://www.na-mic.org
Behnood Gholami
Marc Niethammer
Oleg Michaelovich
Namrata Vaswami
Peter Karasev
Arie Nakhmani
Yogesh Rathi
Patricio Vela
Vandana Mohan
Shawn Lankton
Gozde Unal
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 flythroughs)
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 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
National Alliance for Medical Image Computing
http://www.na-mic.org
Comparison
Lung segmentation: image ROI [503 333 43]
3 rounds of interaction/editing
Method
Segmentation time (seconds)
Memory
(MB)
1st edit
2nd edit
3rd edit
GrowCut
210
255
269
200
Proposed
28
3
3
522
GrowCut:
Proposed:
National Alliance for Medical Image Computing
http://www.na-mic.org
Quantitative
Dice
Vol. Overlap
97%
97%
Particle Filtering
National Alliance for Medical 7Image Computing
http://www.na-mic.org
April 15
Particle Filtering
8
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 ablation
Group 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.
National Alliance for Medical Image Computing
http://www.na-mic.org
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