Difference between revisions of "2012 Summer Project Week:UtahAutoScar"
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| − | We are developing methods for automatically detecting post-procedural scar in LGE-MRI images. The goal is | + | We are developing methods for automatically detecting post-procedural scar in LGE-MRI images. The goal is to make statistical group comparisons based on the spatial distribution and amount of scar. |
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<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | Our approach for | + | Our approach for automatic scar segmentation is summarized in the SPIE 2012 reference below. Briefly, we are using k-means clustering to tease apart the normalized pixel intensities corresponding to different tissue types (e.g., healthy, scar, blood). |
Our plan for the project week is to test our module, and ensure that it meets "Ron's Rules" and develop supporting documentation/examples. We will present a tutorial on this module during the project week tutorial contest [http://wiki.na-mic.org/Wiki/index.php?title=CARMA-LA-Scar_TutorialContestSummer2012] | Our plan for the project week is to test our module, and ensure that it meets "Ron's Rules" and develop supporting documentation/examples. We will present a tutorial on this module during the project week tutorial contest [http://wiki.na-mic.org/Wiki/index.php?title=CARMA-LA-Scar_TutorialContestSummer2012] | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | Software for the automatic scar segmentation has been implemented. We | + | Software for the automatic scar segmentation has been implemented [http://wiki.na-mic.org/Wiki/index.php/DBP3:Utah:SlicerModuleAutoScar]. We have created documentation/tutorial for the module. Lastly, we have updated the acknowledgements section to include the appropriate logos and information. The module is now available as a loadable extension in Slicer 4. |
Latest revision as of 20:07, 21 June 2012
Home < 2012 Summer Project Week:UtahAutoScar
Updated module with logos/acknowledgements
Automatic Left Atrial Scar Segmentation
Key Investigators
- Utah: Danny Perry, Alan Morris, Josh Cates, Rob MacLeod
Objective
We are developing methods for automatically detecting post-procedural scar in LGE-MRI images. The goal is to make statistical group comparisons based on the spatial distribution and amount of scar.
Approach, Plan
Our approach for automatic scar segmentation is summarized in the SPIE 2012 reference below. Briefly, we are using k-means clustering to tease apart the normalized pixel intensities corresponding to different tissue types (e.g., healthy, scar, blood).
Our plan for the project week is to test our module, and ensure that it meets "Ron's Rules" and develop supporting documentation/examples. We will present a tutorial on this module during the project week tutorial contest [1]
Progress
Software for the automatic scar segmentation has been implemented [2]. We have created documentation/tutorial for the module. Lastly, we have updated the acknowledgements section to include the appropriate logos and information. The module is now available as a loadable extension in Slicer 4.
Delivery Mechanism
This work will be delivered to the NA-MIC Kit as a
- ITK Module - NO
- Slicer Module
- Built-in - NO
- Extension -- commandline - YES
- Extension -- loadable - NO
- Other (Please specify)
References
- Daniel Perry, Alan Morris, Nathan Burgon, Christopher McGann, Robert MacLeod, Joshua Cates. "Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation". SPIE Medical Imaging: Computer Aided Diagnosis, Feb 2012.
