2011 Summer Project Week Segmentation TBI
Result of supervised segmentation - acute images
Result of supervised segmentation - follow-up images
- Figure 1.
- Newttt1.png
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- Newttt2.png
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- Newttt3.png
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- Newttt4.png
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Full Title of Project
Key Investigators
- Utah: Bo Wang, Marcel Prastawa, Guido Gerig
- UCLA: Jack Van Horn, Andrei Irimia, Micah Chambers
Objective
Traumatic brain injury (TBI) is a driving biological problem (DBP) in NA-MIC. It occurs when an external force traumatically injures the brain. TBI is a major cause of death and disability worldwide, especially in children and young adults.
We're working on the supervised segmentation and atlas optimization of longitudinal TBI data.
On anatomical MRI scans, to quantitatively analyze the cortical thickness, white matter changes, we need to have a good segmentation on TBI images. However, for TBI data, standard automated image analysis methods are not robust with respect to the TBI-related changes in image contrast, changes in brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.
Approach, Plan
Our plan for the project week:
- Test our preliminary code
- Discuss with collaborators and try to refine the algorithm
Progress
- We applied the preliminary algorithm to current data and fix some bugs in the code.
- Our collaborators helped us to validate our current results of supervised segmentation. We got some comments and feedback from our collaborators, these are very important for us to improve the current algorithm.
- After discussing with Andrei and Micah about the TBI data, we know more clear about what the clinicians need.
- We got some new data for further testing of our algorithm.
