2012 Summer Project Week:UtahAutoScar - Revision history

Ggardner at 20:07, 21 June 2012

2012-06-21T20:07:28Z

Ggardner at 20:05, 21 June 2012

2012-06-21T20:05:26Z

Ggardner at 19:36, 21 June 2012

2012-06-21T19:36:04Z

Ggardner at 00:33, 10 June 2012

2012-06-10T00:33:13Z

Ggardner at 00:23, 10 June 2012

2012-06-10T00:23:21Z

Ggardner at 00:17, 10 June 2012

2012-06-10T00:17:54Z

Ggardner: Created page with 'NOTOC Image:PW-MIT2012.png|Projects List Image:carma_afib_auto_scar.png|Automatic Left Atrial Scar Segmentation ==…'

2012-06-10T00:17:05Z

Created page with '__NOTOC__ <gallery> Image:PW-MIT2012.png|Projects List Image:carma_afib_auto_scar.png|Automatic Left Atrial Scar Segmentation </gallery> ==…'

New page

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
Image:carma_afib_auto_scar.png|Automatic Left Atrial Scar Segmentation
</gallery>

==Key Investigators==
* Utah: Danny Perry, Alan Morris, Josh Cates, Rob MacLeod

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for automatically detecting post-procedural scar in LGE-MRI images. The goal is to be able to make statistical group comparisons based on the spatial distribution and amount of scar.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors 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]

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the automatic scar segmentation has been implemented. We need to create documentations and tutorials as well as finalize the migration to Slicer 4. We have begun statistical analysis and validation of the module.

</div>
</div>

==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.

← Older revision		Revision as of 20:07, 21 June 2012
Line 35:		Line 35:

	<h3>Progress</h3>		<h3>Progress</h3>
−	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.	+	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.

← Older revision		Revision as of 20:05, 21 June 2012
Line 35:		Line 35:

	<h3>Progress</h3>		<h3>Progress</h3>
−	Software for the automatic scar segmentation has been implemented [http://wiki.na-mic.org/Wiki/index.php/DBP3:Utah:SlicerModuleAutoScar]. We ~~need to create documentations and tutorials as well as finalize~~ the ~~migration to Slicer 4~~. We have ~~begun statistical analysis~~ and ~~validation of the module~~.	+	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.

← Older revision		Revision as of 00:33, 10 June 2012
Line 12:		Line 12:

	<h3>Objective</h3>		<h3>Objective</h3>
−	We are developing methods for automatically detecting post-procedural scar in LGE-MRI images. The goal is ~~to be able~~ to make statistical group comparisons based on the spatial distribution and amount of scar.	+	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.

← Older revision		Revision as of 00:17, 10 June 2012
Line 34:		Line 34:

	<h3>Progress</h3>		<h3>Progress</h3>
−	Software for the automatic scar segmentation has been implemented. We need to create documentations and tutorials as well as finalize the migration to Slicer 4. We have begun statistical analysis and validation of the module.	+	Software for the automatic scar segmentation has been implemented [http://wiki.na-mic.org/Wiki/index.php/DBP3:Utah:SlicerModuleAutoScar]. We need to create documentations and tutorials as well as finalize the migration to Slicer 4. We have begun statistical analysis and validation of the module.

@@ Line 2: / Line 2: @@
 <gallery>
 Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
 Image:carma_afib_auto_scar.png|Automatic Left Atrial Scar Segmentation
 </gallery>

@@ Line 25: / Line 25: @@
 <h3>Approach, Plan</h3>
-Our approach for analyzing diffusion tensors 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 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]