Stanford Simbios group - Revision history

Spal5: /* Aim */

2010-06-07T05:09:25Z

Aim

Spal5: /* Process Flowchart */

2010-06-07T05:06:43Z

Process Flowchart

Spal5: /* */

2010-06-07T05:02:58Z

Spal5: /* */

2010-06-07T04:56:09Z

Spal5: /* */

2010-06-07T04:50:55Z

Spal5: /* Update June 05, 2010 */

2010-06-07T04:31:26Z

Update June 05, 2010

Spal5: /* Update June 05, 2010 */

2010-06-07T04:27:54Z

Update June 05, 2010

Spal5: /* Update June 05, 2010 */

2010-06-06T18:42:45Z

Update June 05, 2010

Spal5: /* Aim */

2010-06-06T18:21:41Z

Aim

Spal5: /* Update June 05, 2010 */

2010-06-05T19:46:40Z

Update June 05, 2010

@@ Line 16: / Line 16: @@
 </gallery>
-==Aim==
+==Aims==
 The purpose of this project is to develop a methodology to rapidly construct three-dimensional anatomical structures from magnetic resonance (MR) imaging for clinical diagnosis. We are currently interested in segmenting hip bone geometry of patients prior to femoral acetabular impingement surgery. The clinical motivation for this study is to eliminate the need for computed tomography (CT) scanning and associated radiation dose in patients during surgical planning.

← Older revision		Revision as of 05:06, 7 June 2010
Line 26:		Line 26:

	==Process Flowchart==		==Process Flowchart==
		+
		+	The pipeline shows the steps required for segmentation of hip joint structures. The steps in red represent user interactions.
		+
	[[Image:flowchart_pipeline.jpg\|left\|thumb\| Segmentation Pipeline]]		[[Image:flowchart_pipeline.jpg\|left\|thumb\| Segmentation Pipeline]]

@@ Line 52: / Line 52: @@
 . Currently, we are able to extract the hip joint structures that closely match MR data with minimal user interaction. However, to improve the accuracy of our segmentation, we are implementing a manual refinement step. Using a GUI-based interface, a user will be able to improve on the output of the modeling pipeline.
-. Evaluate the accuracy of our MR-based segmentation with geometry acquired from CT imaging. We will quantify the accuracy of our method by comparing the number of pixels identified within a structure compared
+. Evaluate the accuracy of our MR-based segmentation with geometry acquired from CT imaging. We will quantify the accuracy of our method by calculating the percentage difference in enclosed volumes between MR- and CT-based geometries.
 ==Atlas Generation from Input MR Images==

@@ Line 48: / Line 48: @@
 . We implemented a semi-automatic segmentation pipeline using a combination of ITK filters and custom scripting. The inputs to the pipeline were IDEAL SPGR Fat and Water MR images. The outputs were 3D outlines of regions-of-interest.
-Next step:
+Next steps:
-Currently, we are able to extract the hip joint structures that closely match MR data with minimal user interaction. However, to improve the accuracy of our segmentation, we are implementing a manual refinement step. Using a GUI-based interface, a user will be able to improve on the output of the modeling pipeline.
+. Currently, we are able to extract the hip joint structures that closely match MR data with minimal user interaction. However, to improve the accuracy of our segmentation, we are implementing a manual refinement step. Using a GUI-based interface, a user will be able to improve on the output of the modeling pipeline.
 ==Atlas Generation from Input MR Images==

@@ Line 46: / Line 46: @@
 . We optimized an MR protocol to maximize signal-to-noise ratio of the pelvic region imaging with a cardiac body coil. We experimented with different scan sequences and found an IDEAL SPGR sequence to provide the best contrast for our application. We imaged 6 healthy subjects to acquire their IDEAL SPGR fat and water images. The total scan time per subject is about 20 minutes.
-. We implemented a semi-automatic segmentation pipeline using a combination of ITK filters and custom scripting. The inputs to the pipeline were IDEAL SPGR Fat and Water MR images. The output was a 3D outline of a region-of-interest.
+. We implemented a semi-automatic segmentation pipeline using a combination of ITK filters and custom scripting. The inputs to the pipeline were IDEAL SPGR Fat and Water MR images. The outputs were 3D outlines of regions-of-interest.
 ==Atlas Generation from Input MR Images==

@@ Line 32: / Line 32: @@
 ==='''Update June 05, 2010'''===
-Recent work:
+[[Image:07_femur_filled.JPG|left|thumb| Segmented Femur]]
-. We optimized an MR protocol to maximize signal-to-noise ratio of the pelvic region imaging with a cardiac body coil. We experimented with different scan sequences and found an IDEAL SPGR sequence to provide the best contrast for our application. We imaged 6 healthy subjects to acquire their IDEAL SPGR fat and water images. The total scan time per subject is about 20 minutes.
+[[Image:07_femur_outline.JPG|left|thumb| Segmented Femur Outline]]
-. We implemented a semi-automatic segmentation pipeline using a combination of ITK filters and custom scripting. The inputs to the pipeline were IDEAL SPGR Fat and Water MR images. The output was a 3D outline of a region-of-interest.
+[[Image:07_acetabularcup_filled.JPG|left|thumb| Segmented Acetabular Cup]]
-[[Image:07_femur_filled.JPG|left|thumb| Segmented Femur]]
+==''==
-[[Image:07_femur_outline.JPG|left|thumb| Segmented Femur Outline]]
+Recent work:
-[[Image:07_acetabularcup_filled.JPG|left|thumb| Segmented Acetabular Cup]]
+. We optimized an MR protocol to maximize signal-to-noise ratio of the pelvic region imaging with a cardiac body coil. We experimented with different scan sequences and found an IDEAL SPGR sequence to provide the best contrast for our application. We imaged 6 healthy subjects to acquire their IDEAL SPGR fat and water images. The total scan time per subject is about 20 minutes.
-[[Image:07_acetabularcup_outline.JPG|left|thumb| Segmented Acetabular Cup Outline]]
+. We implemented a semi-automatic segmentation pipeline using a combination of ITK filters and custom scripting. The inputs to the pipeline were IDEAL SPGR Fat and Water MR images. The output was a 3D outline of a region-of-interest.
 ==Atlas Generation from Input MR Images==

@@ Line 17: / Line 17: @@
 ==Aim==
-The purpose of this project is to develop a methodology to rapidly construct three-dimensional anatomical structures from magnetic resonance (MR) imaging for clinical diagnosis. We are currently interested in segmenting hip bone geometry of patients prior to femoral acetabular impingement surgery. The clinical motivation for this study is to eliminate the need for computed tomography (CT) scanning, and associated radiation dose, in patients during surgical planning.
+The purpose of this project is to develop a methodology to rapidly construct three-dimensional anatomical structures from magnetic resonance (MR) imaging for clinical diagnosis. We are currently interested in segmenting hip bone geometry of patients prior to femoral acetabular impingement surgery. The clinical motivation for this study is to eliminate the need for computed tomography (CT) scanning and associated radiation dose in patients during surgical planning.
 The specific aims are -