CIP and Nipype - Revision history

Rharmo at 16:27, 9 January 2015

2015-01-09T16:27:27Z

Rharmo at 02:07, 9 January 2015

2015-01-09T02:07:22Z

Rharmo: /* Project Description */

2015-01-09T01:35:31Z

Project Description

Rharmo: /* Project Description */

2015-01-09T01:25:01Z

Project Description

Rharmo: /* Project Description */

2015-01-05T20:38:52Z

Project Description

Rharmo at 00:01, 4 January 2015

2015-01-04T00:01:07Z

Rharmo at 23:58, 3 January 2015

2015-01-03T23:58:11Z

Rharmo at 23:55, 3 January 2015

2015-01-03T23:55:36Z

Rharmo at 22:40, 3 January 2015

2015-01-03T22:40:22Z

Raul: Created page with 'NOTOC Image:PW-2015SLC.png|Projects List ==Key Investigators== ==Project Description==
Projects List </gallery> ==Key Investigators== ==Project Description== <div style="margin: 20px;…'

New page
NOTOC
<gallery>
Image:PW-2015SLC.png|[[2015_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==

==Project Description==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
*
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
*
</div>
</div>

@@ Line 18: / Line 18: @@
 * Define and implement a set of workflows (nipype/Vistrails) for the following tasks:
 ** computing body composition (ex pectoralis muscles, subcutaneous fat, visceral fat, paravertebral muscles...) phenotypes from pre-labeled CT data. The phenotypes consist of cross sectional areas of each label and CT intensity statistics within the labeled region
-[[File:labeledCT.png]]
+[[File:LungParenchyma.png|290px]]
 ** computing lung parenchyma phenotypes from CT data
@@ Line 26: / Line 26: @@
 * Generated nipype interfaces for the CLIs available as part of the chest imaging platform and for python and for python classes
 * Implemented an example workflow for the generation of lung parenchyma phenotypes
-[[File:parenchyma_workflow_graph.dot.png]]
+[[File:parenchyma_workflow_graph.dot.png|290px]]
 </div>
 </div>

@@ Line 26: / Line 26: @@
 * Generated nipype interfaces for the CLIs available as part of the chest imaging platform and for python and for python classes
 * Implemented an example workflow for the generation of lung parenchyma phenotypes
-[[File:labeledCT.png]]
+[[File:parenchyma_workflow_graph.dot.png]]
 </div>
 </div>

@@ Line 15: / Line 15: @@
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Approach, Plan</h3>
-* Our first is to generate nipype interfaces from the slicer CLIs and python scripts
+* Our first task is to generate nipype interfaces from the slicer CLIs and python scripts
 * Define and implement a set of workflows (nipype/Vistrails) for the following tasks:
 ** computing body composition (ex pectoralis muscles, subcutaneous fat, visceral fat, paravertebral muscles...) phenotypes from pre-labeled CT data. The phenotypes consist of cross sectional areas of each label and CT intensity statistics within the labeled region
@@ Line 24: / Line 24: @@
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Progress</h3>
+* Generated nipype interfaces for the CLIs available as part of the chest imaging platform and for python and for python classes
 </div>
 </div>

@@ Line 11: / Line 11: @@
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Objective</h3>
-* We now have a suite of CLIs and python scripts for the processing and the analysis of chest images ready to be incorporated in Slicer as part of the Chest Imaging Platform Extension. This week we will be specifically focusing on defining clinically relevant chest image processing workflows that utilize the CLIs and scripts and implementing the workflows in nipype for their deployment in high performance computing environments.
+* We now have a suite of CLIs and python scripts for the processing and the analysis of chest images ready to be incorporated in Slicer as part of the Chest Imaging Platform Extension. This week we will be specifically focusing on defining clinically relevant chest image processing workflows that utilize the CLIs and scripts and implementing the workflows in nipype or Vistrails for their deployment in high performance computing environments.
 </div>
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Approach, Plan</h3>
 * Our first is to generate nipype interfaces from the slicer CLIs and python scripts
-* Define and implement a set of workflows for the following tasks:
+* Define and implement a set of workflows (nipype/Vistrails) for the following tasks:
 ** computing body composition (ex pectoralis muscles, subcutaneous fat, visceral fat, paravertebral muscles...) phenotypes from pre-labeled CT data. The phenotypes consist of cross sectional areas of each label and CT intensity statistics within the labeled region
 [[File:labeledCT.png]]

@@ Line 1: / Line 1: @@
 <gallery>
 Image:PW-2015SLC.png|[[2015_Winter_Project_Week#Projects|Projects List]]
 </gallery>
@@ Line 18: / Line 18: @@
 * Define and implement a set of workflows for the following tasks:
 ** computing body composition (ex pectoralis muscles, subcutaneous fat, visceral fat, paravertebral muscles...) phenotypes from pre-labeled CT data. The phenotypes consist of cross sectional areas of each label and CT intensity statistics within the labeled region
 ** computing lung parenchyma phenotypes from CT data

@@ Line 5: / Line 5: @@
 ==Key Investigators==
+Rola Harmouche, James Ross, Alex Yarmakovich
 ==Project Description==