Difference between revisions of "2011 Summer Project Week NerveSeg"

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Successful segmentation including afterall
 
Successful segmentation including afterall
 
Image:NerveSeg-peripheral.png|Figure 6 - Example_3: Another example, following several nerve bundles.
 
Image:NerveSeg-peripheral.png|Figure 6 - Example_3: Another example, following several nerve bundles.
Image:NerveSeg-map.png|Figure 7 - Example_4: A map of several automatically segmented nerves with herniated disc manually segmented.
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Image:NerveSeg-mapp.png|Figure 7 - Example_4: A map of several automatically segmented nerves with herniated disc manually segmented.
 
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Revision as of 21:22, 23 June 2011

Home < 2011 Summer Project Week NerveSeg

Nerve Segmentation




Key Investigators

  • MIT: Adrian Dalca, Polina Golland
  • BWH: Giovanna Danagoulian, Ehud Schmidt
  • SPL: Ron Kikinis

Objective

We are developing a nerve segmentation algorithm for the automatic isolation of nerves and nerve ganglia inside the spinal sack and out through the vertebrae in new MR Myelography images. Current progress can track the core of a Nerve.


Approach, Plan

Currently we use a particle-filter tracking approach for segmenting the nerves. The algorithm is given a seed point, preferably somewhere in the spine. The particles are tubes following Bézier curves (and hence forming a B-spline track). The dynamics model encourages continuity and smoothness. The image likelihood model compares gradient fields and intensities of predicted patches with image observations to evaluate a posterior distribution of the particles' importance. While we can currently usually track the nerve cores, usually fully throughout the vertebral canal, the algorithm does not delineate the full extent of the nerves, and behaves poorly on peripheral nerves outside of the vertebral canal. We will modify the likelihood function and parameters and concentrate on achieving this during the project week.

Progress