Stanford SIMBIOS: Whole Body Segmentation for Simulation

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Example whole-body CT dataset from WashU displayed in 3D Slicer.
Articulated musculoskeletal models.

Key Investigators

  • Stanford: Harish Doddi, Saikat Pal
  • WashU: Daniel Marcus
  • Harvard: Ron Kikinis
  • Steve Pieper, Isomics, Inc.



The aim of this project is to develop an automatic/semi-automatic methodology to convert whole body imaging datasets into three-dimensional models for neuromuscular biomechanics and finite element simulations. Initially, we will investigate the existing capabilities in EMSegmenter software to automatically segment the knee joint.

Approach, Plan

Investigate the existing functionality of EMSegementer to extract whole body models from CT and MR datasets. Initial efforts will be focused on developing atlases of specific joints (e.g. the knee) and evaluating EMSegmenter algorithms. The plan is to have imported MRI knee geometries in EMSegmenter and create an average atlas before the project week. During the project week, the EMSegmenter algorithm will be tested on a specific subject geometries.


  • Compiled a set of 5 knee MRI data-sets with manually segmented volumes.
  • Evaluated registration algorithms to develop an averaged atlas of the knee joint. We evaluated the rigid and affine registrations techniques in Slicer, and attempted a diffeomorphic deamons algorithm to align multiple data-sets.
  • Evaluated EMSegmenter to create atlas-independent image segmentation.
  • We are in the process of converting manually-segmented models to a knee atlas.