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SEGMENTATION

SEGMENTATION. METHODS & APPLICATIONS. Workflow of fMRI analysis. - Raw data Sequence parameters Noise / Inhomogeneities. Freq [Hz]. - Volume normalization Inhomogeneity correction. Time [$]. Pre-processing. Segmentation WM / GM / CSF. Volumetric Studies.

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SEGMENTATION

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  1. SEGMENTATION METHODS & APPLICATIONS

  2. Workflow of fMRI analysis • - Raw data • Sequence parameters • Noise / Inhomogeneities Freq [Hz] • - Volume normalization • Inhomogeneity correction Time [$] Pre-processing Segmentation WM / GM / CSF Volumetric Studies - Cortical mesh reconstruction - Mesh inflation / Flattening Statistical analysis Rendering

  3. Outline • Segmentation Algorithms • Software tools • Comparative Analysis of the tools • Applications

  4. Factors • MRI aids segmentation because of the high contrast between different tissues • Different components in an object can be highlighted by carefully choosing relaxation timings and RF pulses • Under ideal imaging conditions should output images with small number of distinct classes • BUT..

  5. Contd.. The image is degraded by -intensity inhomogenities in the field -partial volume effects Causing the classes to overlap in the image intensity histogram

  6. Segmentation Approaches -Structural -Edge detection algorithms -Region growing techniques -Statistical -parametric -non-parametric

  7. Software Tools -Analysis of Functional Neuroimages (AFNI) -Statistical Parametric Mapping (SPM) -FMRIB’s Software Library (FSL) -Freesurfer

  8. Segmentation Outline -Intensity non-uniformity correction -Brain extraction -Normalization to template brain (Can create your own data specific template) -Gaussian Mixtures model with the Expectation Maximization approach

  9. Preprocessing • Nonuniformity intensity correction • During processing an iterative approach is employed to estimate the multiplicative bias field and the distribution of true tissue intensities.

  10. Highlights -Fast and efficient computations -Robust Algorithm implementation -Good GM/WM segmentation -The algorithms limits the no. of classes defined in an image -Assumes Gaussian distributions which will perform badly with a noisy dataset

  11. FSL -Inhomogenity correction -Brain Extraction -Non-parametric segmentation with EM approach -No assumption about the underlying distribution -Accounts spatial information

  12. Highlights -Fast & efficient -User can define the no. of classes (can account for lesions etc along with GM, WM, CSF classification) -Initial thresholding does not work well with noisy data

  13. SPM - FSL with and without prior probability maps

  14. AFNI Gyrus Finder -Does a good job at regional segmentation -Slow and not so accurate for whole brain segmentation -requires a lot of manual editing Freesurfer -Very Slow (requires about ~30 mins per brain) -does not do a good job segmenting the GM -requires a lot of user intervention

  15. Statistics -Output of segmentation is a mask for each of the classes defined -ROI analysis can be done using the above as binary masks -For particular requirements the user can delineate regions and use them for the statistics

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