NA-MIC Work of Tannenbaum Group

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NA-MIC Work of Tannenbaum Group. Computer Science and Mathematics Stony Brook University. 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 - PowerPoint PPT Presentation

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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

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 20, 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.