2009 Summer Project Week Project Segmentation of Muscoskeletal Images
Knee MRI Image
Filled Femur Label Map
EM Segmented Output
64 Affine Registered on 58
64 Pipeline Affine Registered on 58
64 Bspline Registered on 58
Masked 42 MRI
Masked 64 MRI
Registered Image Difference of 42 and 64
BSpline Registered Output of 42 and 64
Key Investigators
- Stanford: Harish Doddi, Saikat Pal, Scott Delp
- Harvard: Ron Kikinis
- Steve Pieper, Isomics, Inc.
- Luis Ibanez, Kitware, Inc.
Objective
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.
Specific Aims
- Explore the segmentation techniques for knee MRI datasets with special focus on patella, femur and tibia bones.
Approach, Plan
We are working on understanding the capabilities of RegisterImage module in Slicer to apply image-to-image registration on knee datasets. We are implementing masking algorithms to isolate specific knee bones, and perform parameter exploration to evaluate the sensitivity of registered images to input parameters. We are exploring the feasibility of applying python ICP-based registration algorithms to directly morph a surface model to a target image geometry.
Our goals for the project week are:
- Implement an algorithm to acquire masked regions of interest from MR datasets.
- Perform and evaluate results from a parameter space exploration study of RegisterImages Batchmake module on knee dataset.
- Resolve issues in building Python modules from slicer source code.
- Explore possibility of model-to-image registration using existing atlas (.vtk, .stl) to a target image using Python ICP Registration module.
Progress
