KSlice Interactive Segmentation - National Alliance for Medical

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:

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Quantitative Dice

Vol. Overlap

97%

97%

Particle Filtering

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April 15

Particle Filtering

8

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Particle Filtering Registration

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Longitudinal shape analysis

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Traumatic Brain Injury

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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

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Results

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

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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