TLEMSafe
Grant Number: FP7-ICT-2009-4
Deliverable Report
D2.2: Software tool for the extraction of relevant muscle parameters
Author:
Date:
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Roel Wirix-Speetjens, Materialise NV
26/01/2012
P
CO
1.0
1. Objective
Deliverable 2.2 relates to task 2.3: Automate parameterization of MRI images. In this task, a method
is developed to (semi)automatically extract the essential muscle parameters (bone geometries,
muscle insertion points, muscle shapes and volumes) from a 3D MRI image.
2. Method
The method that we originally envisaged for obtaining the muscle parameters from MRI data uses
the Active Shape Model (ASM) technique to construct a parameterized model of the lower limb.
From literature it is know that this technique shows high segmentation accuracy. The major
disadvantage of this technique is the large number of (labeled) datasets that is requires. Preliminary
results of the sensitivity analysis performed in WP3 proved the assumption that a high accuracy
would be needed to be wrong. Collecting the large number of datasets also seemed very timeconsuming and therefore more difficult than originally planned.
We therefor investigated an alternative method based on non-rigid registration of an MRI atlas to
the patient MRI data. In this method, we first construct an atlas (an image volume that is completely
segmented, i.e. for every voxel it is known to which anatomical structure it belongs). The
construction of the atlas was completed in task 2.1. We then register this atlas non-rigidly to the
patient image. This yields an automatic segmentation of all relevant anatomical structures of the
patient (bones, cartilage, muscles, tendons). Instead of labeling each voxel, we could also manually
segment the model and then apply the non-rigid transformations to the corresponding STLs. The
method for non-rigid registration of the MRI images uses the well-known mutual information
technique. A C++ based prototype based on the ITK kernel has been developed by MAT incorporation
this technique.
3. Results
The table below gives an overview of the registration results for the 10 healthy subjects collected in
WP1. For each experiment, the atlas of healthy subject 1 was used. One can see that, in particular,
the results of Healthy Subject 1b shows a good registration result, which is expected as it is the same
subject but scanned at a later date. The result of e.g. Healthy Subject 5 on the other hand is worse,
but this can be explained by a difference in age and gender between both subjects.
Future work includes fine-tuning the method to improve the results of e.g. Healty Subject 5 and the
integration of this technique in a Mimics prototype.
Transversal
Sagittal
Coronal
Healthy001b
Healthy002
Healthy003
Healthy004
Healthy005
Healthy006
Healthy007
Healthy008
Healthy009
Healthy010