Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images By K.M. Pohl, W.M. Wells, A. Guimond, K. Kasai, M.E.

Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

By

K.M. Pohl, W.M. Wells, A. Guimond, K. Kasai, M.E. Shenton, R.

Kikinis, W.E.L. Grimson, and S.K. Warfield

Email: [email protected]

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Overview

• Introduction

• Incorporating Local Prior in EM-MF

• Current Implementation – Tools and Tricks

• Possible Advancements

• Conclusion

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Goal

SPGR

T2W

Tissue Atlases

The Magic AutomaticSegmenter

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EM-MF AlgorithmM-Step

E-Step

Smooth Bias

Image

Correct Intensities

MF-StepRegularize Weights

Estimate TissueProbability

Label Map

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Mean Entropy Atlas

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Merging MEA with SPGR

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Bias

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Bias in Color

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3 D View of SPGR

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Including Local Priors

2. Step

1. Step

Bra

in A

tlas

Cas

e

Registration

Pro

bab

ility

Map

s

Align Atlas

3. Step

M-Step

E-Step

Bias

Correct

MF-Step

Estimate

Label Map

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eP(Tissue T)

* P(GV[x][y][z] | Distribution of T,Bias)

EM Algorithm

P(Tissue T | Position [x][y][z])

Local Prior

Estimating the Tissue Class

WT[x][y][z]

* eEnergy(WT[x][y][z] | Neighboring W)

MF-Approximation

GV[x][y][z] = Grey Value at position [x][y][z] WT [x][y][z] = Weights for tissue class T at position [x][y][z]

+

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Comparing different Segmenter

Registration

only

EM

-MF

Affin

e EM

-MF

Non

Rig

id E

M-M

F

2 Channel Input - Segmenting up to 7 tissue classes

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2 Channel Segmentation with Patient Case and 11 Tissue Classes

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Correction of 1 Channel EM-MF-LP through Specialist

Background CSVSkin Grey Matter White Matter

Right/Left Amygdala Right/Left Superior Temporalgyrus

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Comparing Manual to EM-MF-LP of the STG

Rater A Rater BE

M-M

F-L

P

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Current Installation• Algorithm is a VTK Filter integrated in Slicer • MF Approximation:

– Multi Threaded

– Lookup Table for Gaussian Distribution

• Using several Relaxation Methods instead of the Mean Field Energy Function

• Multi Channel Input (SPGR, T2 , PD)• Train Tissue Definition, e.g. CIM, Distribution • Interface to Matlab

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EM-MF in Slicer

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Tabs of GUI Overview Class Definition Class Interaction EM Settings

Skill Level

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Possible Improvements• Registration Step:

– After each segmentation re-register case with atlas

• E Step– Include shape and topology information in weight

calculation

– Use local class interaction matrix

• M Step:– Use several other filters to smooth bias, e.g. Box Filter,

Pascal Triangle, …

– Include “trash tissue class” where pixels get assigned if all weights are low Bias does not get corrupted

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Conclusion

• Made EM-MF Algorithm more robust• Segmented tissue classes with overlapping gray

value distributions• Included spatial/atlas information into E-Step• Cortex pacellation possible• Future Work: Validating Segmentation