Difference between revisions of "2010 Winter Project Week Musco Skeletal Segmentation"

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<h3>Objective</h3>
 
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The aim of this project is to develop an semi-automatic rapid methodology to convert whole body imaging datasets into three-dimensional geometries for modeling simulations. We have used a multi-contrast MR Segmentation approach to cluster the structures of interest.  The output label maps will be smoothed and existing surface meshes will be morphed on to a target image.
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The aim of this project is to develop a methodology for rapid segmentation of knee structures from magnetic resonance (MR) images for subject-specific modeling. The overall goal can be broken down into two specific objectives -
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1. Rapid segmentation of target structures into label maps.
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2. Generation of simulation-ready models from label maps of individual structures.
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Revision as of 00:40, 4 January 2010

Home < 2010 Winter Project Week Musco Skeletal Segmentation

  • Ideal Spgr Fat MR image

  • Ideal Spgr Water MR image

  • Segmentation Output

  • Bones

  • Cartilage

  • SpgrWater_Hip

  • SpgrFat_Hip

  • Hip Output


Key Investigators

  • Stanford: Harish Doddi, Saikat Pal, Scott Delp
  • Kitware: Luis Ibanez

Objective

The aim of this project is to develop a methodology for rapid segmentation of knee structures from magnetic resonance (MR) images for subject-specific modeling. The overall goal can be broken down into two specific objectives - 1. Rapid segmentation of target structures into label maps. 2. Generation of simulation-ready models from label maps of individual structures.


Approach, Plan

Approach:
Multi-Contrast MR images are collected and seed points for each region of interest are taken as input. Cluster center and standard deviation are calculated for each ROI based on pixel intensities of the seed points. The pixels are clustered based on different pixel intensity values in multiple MR images to the nearest cluster center radius.

For detailed approach, please visit http://www.na-mic.org/Wiki/index.php/Stanford_Simbios_group


Plan:
a. Smooth the existing segmented label maps
b. Build models for bones and cartilage from the existing segmented label maps.

Progress

Finished segmenting different regions of interest like bones, cartilage etc.

Created label maps from existing segmented output.

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