AHM2009:Mind - Revision history

Hjbockholt: /* Detailed Information about the Pipeline */

2009-01-08T15:35:44Z

Detailed Information about the Pipeline

Hjbockholt: /* Overview */

2009-01-08T15:30:50Z

Overview

Mscully: /* Overview */

2009-01-08T15:16:52Z

Overview

Hjbockholt: /* Overview */

2009-01-08T08:22:17Z

Overview

Hjbockholt: /* Overview */

2009-01-08T08:18:43Z

Overview

Hjbockholt: /* Overview */

2009-01-08T08:17:08Z

Overview

Hjbockholt: /* Software & documentation */

2009-01-08T08:10:57Z

Software & documentation

Hjbockholt: /* Overview */

2009-01-08T08:08:25Z

Overview

Hjbockholt: /* Overview */

2009-01-08T08:07:35Z

Overview

Hjbockholt: /* Overview */

2009-01-08T07:58:46Z

Overview

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 ==Detailed Information about the Pipeline==
 *Illustrate the components and workflow of the pipeline using your own data
 **Training [[Image:Sle_wmls_model_generation.png|thumb|280px|Training]]
 ***Approximately 90 features were computed based on the T1, T2, and FLAIR, including neighborhood means with varying radii, mathematical morphometry dilation and erosion, kmeans clustering, and gradients, among others

@@ Line 18: / Line 18: @@
 **The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for human manual raters to agree on fuzzy boundaries across different image contrasts (e.g., T1, T2, FLAIR).
-A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past May in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmentation contest, participants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site, real-time competition on additional novel data sets.
+A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past September in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmentation contest, participants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site, real-time competition on additional novel data sets.
 Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(S. Wells), k-means/bayesian(V. Magnotta), outlier detection(M. Prastawa), etc.), we think we have honed in on a novel methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.

@@ Line 16: / Line 16: @@
 * How does the pipeline compare to state of the art?
-**The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for humans manual raters to agree on fuzzy boundaries across different image contrasts (e.g., T1, T2, FLAIR).
+**The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for human manual raters to agree on fuzzy boundaries across different image contrasts (e.g., T1, T2, FLAIR).
 A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past May in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmentation contest, participants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site, real-time competition on additional novel data sets.

@@ Line 16: / Line 16: @@
 * How does the pipeline compare to state of the art?
-**The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for humans manual raters to agree on fuzzy boundaries across different image constrasts (e.g., T1, T2, FLAIR).
+**The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for humans manual raters to agree on fuzzy boundaries across different image contrasts (e.g., T1, T2, FLAIR).
-A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past May in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmenation conteset, contestants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site real-time competition on additional novel data sets.
+A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past May in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmentation contest, participants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site, real-time competition on additional novel data sets.
-Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(wells), k-means/bayesian(magnotta), outlier detection(prastawa), etc.), we think we have honed in on a novel methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.
+Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(S. Wells), k-means/bayesian(V. Magnotta), outlier detection(M. Prastawa), etc.), we think we have honed in on a novel methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.
 ==Detailed Information about the Pipeline==

@@ Line 18: / Line 18: @@
 **The state of the art in brain lesion classification is a work in progress in NPSLE, as well as, other disorders. There are many approaches that attempt to solve lesion classification in Multiple Sclerosis (where it is much more common to perform neuroimaging studies). Some of these approaches are automated or semi-automated; however, all automatic approaches suffer a lack of ground truth.  It is difficult for humans manual raters to agree on fuzzy boundaries across different image constrasts (e.g., T1, T2, FLAIR).
-A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge] this past May in the form of a grant challenge in segmentation.  Several groups, including this DBP, competed in lesion segmenation conteset, contestants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site real-time competition on additional novel data sets.
+A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge(organized by Warfield and Styner)] this past May in the form of a grand challenge in segmentation.  Several groups, including this DBP, competed in lesion segmenation conteset, contestants were first provided training data-sets with two manual labellings, their respective approaches were judged in an on-site real-time competition on additional novel data sets.
 Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(wells), k-means/bayesian(magnotta), outlier detection(prastawa), etc.), we think we have honed in on a novel methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.

@@ Line 40: / Line 40: @@
 **[http://www.nitrc.org/frs/download.php/570/LesionSegmentationApplications.tgz tutorial software]
 **[http://www.nitrc.org/frs/download.php/569/LesionSegmentationTutorialData.tgz tutorial data]
-**[http://www.nitrc.org/frs/download.php/571/Slicer3Training_WhiteMatterLesions_v2.0.ppt.pdf url end-user tutorial]
+**[http://www.nitrc.org/frs/download.php/571/Slicer3Training_WhiteMatterLesions_v2.0.ppt.pdf end-user tutorial]
 ==Team==

@@ Line 20: / Line 20: @@
 A good example of the challenge that remains in terms of lesion segmentation was well represented at the 2008 MICCAI Conference [http://www.ia.unc.edu/MSseg/ MS Lesion Segmentation Challenge] this past May in the form of a grant challenge in segmentation.  Several groups, including this DBP, competed by first being providing training data-sets with two manual labellings, approaches were judged on an application set naive to each classification approach.
-Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(wells), k-means/bayesian(magnotta), outlier detection(prastawa), etc.), we think we have honed in on a methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.
+Through participation in the recent lesion challenge, a thorough literature review of recent advances, and trying out several approaches (EM-Segment(wells), k-means/bayesian(magnotta), outlier detection(prastawa), etc.), we think we have honed in on a novel methodology that will perform well for providing lesion maps in NPSLE; in addition, generalize well (following training) for other neurological disorders, such as MS, vascular dementia, and other disorders of the brain.
 ==Detailed Information about the Pipeline==