A Plan for Brain Connectivity Analysis John Melonakos

1National Alliance for Medical Image Computing http://na-mic.org

A Plan for Brain Connectivity Analysis

John Melonakos


Schizophrenia

Kandel, Schwartz, Jessell. “Principles of Neural Science, 4th Edition.” (2000). p.1188


The Plan

1) Segment brain into white matter, gray matter, and CSF

2) Divide resulting gray matter segmentation into key anatomical regions (e.g. the DLPFC)

3) Grow DTI fibers from the key anatomical regions to analyze connectivity


STEP 1: Find WM,GM,CSF

To do this we have chose an approach based on Bayesian Segmentation

Step 1

Data: Probabilities generated by applying a distribution (typically Gaussian) to your dataPriors: An initial guess at the solution

Posteriors: The resulting probabilities

Constant

Priors*DataPosteriors


The Power of Bayes’ Rule

Mumford, “The Bayesian Rationale for Energy Functionals”

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Step 1

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Minimizing the Energy = Increasing the Posterior Probability

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Step 1


The Algorithm

• Goal: Segment Volume into 3 classes• Solution:

1. Create 3 Data terms

2. Guess at 3 Prior terms

3. Apply Bayes’ Rule 3 times

4. Find the maximum of the 3 resulting posteriors to determine the winning class

5. Apply a label for the winning class

Haker, et al. “Knowledge-Based Segmentation of SAR Data with Learned Priors” (1999)Teo, et al. “Creating connected representations of cortical gray matter for functional MRI visualization” (1998)Teo, et al. “Anisotropic diffusion of posterior probabilities” (1997)

Step 1


Added Tricks

• Goal: Segment Volume into ‘N’ classes

• Solution: 1. Create ‘N’ Data terms

2. Guess at ‘N’ Prior terms

3. Apply Bayes’ Rule ‘N’ times

4. Find the maximum of the ‘N’ resulting posteriors to determine the winning class

5. Apply a label for the winning class

Smooth posteriors

before finding the maximum

Iterate multiple times to refine the data and prior terms

Step 1


Project Status

• Fully implemented in ITK code thanks to the Programming Week

• Currently writing a paper for the Insight journal detailing the open source nature of the ITK code (i.e. was able to use code from 14 separate ITK filters)

• Finishing touches still in progress

Step 1


Some Pictures

Raw Result

Step 1


STEP 2: Subdivide GM

Step 2

• Work with Jim Fallon @ UCI (Core 3)


More Sketches

Step 2


Semi-Automated

• Work with Ramsey Al-Hakim on DLPFC Slicer project– Writing code to wrap the ITK Bayesian

filter in VTK for use in our DLPFC Slicer Module

Step 2


STEP 3: DTI Fibers

• Work with Eric and Xavier

Step 3

dss ),( )C(


DTI: Artistic Rendition

Step 3


DTI: More Art

Step 3


The Centrum Ovale Problem

Step 3


DTI Reading

Selected Readings

• Eric Pichon’s HBJ approach

• Basser & LiBihan’s early tensor work

• Dave Tuch’s Q-Ball work

• Isabelle Corouge’s DTI shape models

• and more … (currently taking suggestions)

Step 3


Questions?

Documents

A Plan for Brain Connectivity Analysis John Melonakos