2008 Winter Project Week Image Registration Update - Revision history

Gregsharp: /* Attendees */

2009-01-06T18:46:35Z

Attendees

Aylward: /* Notes and Summary */

2008-02-07T15:58:46Z

Notes and Summary

Gerig: /* Notes and Summary */

2008-01-12T00:45:27Z

Notes and Summary

Gerig: /* Breakout session on "Registration methods in the NAMIC Toolkit" */

2008-01-12T00:40:46Z

Breakout session on "Registration methods in the NAMIC Toolkit"

Gerig: /* Breakout session on "Registration methods in the NAMIC Toolkit" */

2008-01-12T00:38:55Z

Breakout session on "Registration methods in the NAMIC Toolkit"

Gerig: /* Notes and Summary */

2008-01-12T00:34:51Z

Notes and Summary

Gerig: /* Notes and Summary */

2008-01-12T00:34:31Z

Notes and Summary

Gerig: /* Notes and Summary */

2008-01-12T00:34:07Z

Notes and Summary

Gerig: /* Notes and Summary */

2008-01-12T00:33:49Z

Notes and Summary

Gerig: /* Notes and Summary */

2008-01-12T00:33:29Z

Notes and Summary

@@ Line 31: / Line 31: @@
 * Mark Scully
 * David Gobbi
 == Topics ==

← Older revision		Revision as of 15:58, 7 February 2008
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	The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.		The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.
		+
		+	Directions for accessing the current version of the Slicer registration module are online at:
		+	http://www.na-mic.org/Wiki/index.php/ITK_Registration_Optimization#Instructions_for_early_adopters


	Back to [[AHM_2008]], [[Events]]		Back to [[AHM_2008]], [[Events]]

← Older revision		Revision as of 00:45, 12 January 2008
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−		+	Back to [[AHM_2008]], [[Events]]
−	Back to [~~http://www.na-mic.org/Wiki/index.php/~~AHM_2008 ~~\| AHM 2008~~]

← Older revision		Revision as of 00:40, 12 January 2008
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−	Back to ~~AHM 2008~~ [http://www.na-mic.org/Wiki/index.php/AHM_2008]	+	Back to [http://www.na-mic.org/Wiki/index.php/AHM_2008 \| AHM 2008]

← Older revision		Revision as of 00:38, 12 January 2008
Line 87:		Line 87:

	The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.		The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.
		+
		+
		+
		+	Back to AHM 2008 [http://www.na-mic.org/Wiki/index.php/AHM_2008]

@@ Line 84: / Line 84: @@
 The algorithm performs a series of steps from initial alignment over rigid, affine to BSpline. The image is finally transformed by cascading the set of transformations into one transformation step using the interpolation mode of choice.
-Stephen Aylward showed a demonstration of a nonlinear registration of a subject-to-atlas matching with a case that presented difficulties using alternative registration techniques.
+Stephen Aylward showed a demonstration of a nonlinear registration of a subject-to-atlas matching with a case that presented difficulties using previously available registration techniques.
 The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.

@@ Line 76: / Line 76: @@
 == Notes and Summary ==
-Introduction by Guido Gerig.
+Introduction and notes by Guido Gerig.
 Detailed presentation by Stephen Aylward w.r.t. the development of a registration toolkit to Slicer-3 (see status slides above), developed by the team Aylward/Ibanez/Turek.

← Older revision		Revision as of 00:33, 12 January 2008
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	Introduction by Guido Gerig.		Introduction by Guido Gerig.
		+
	Detailed presentation by Stephen Aylward w.r.t. the development of a registration toolkit to Slicer-3 (see status slides above), developed by the team Aylward/Ibanez/Turek.		Detailed presentation by Stephen Aylward w.r.t. the development of a registration toolkit to Slicer-3 (see status slides above), developed by the team Aylward/Ibanez/Turek.

← Older revision		Revision as of 00:33, 12 January 2008
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	== Notes and Summary ==		== Notes and Summary ==

−	Introduction by Guido Gerig. Detailed presentation by Stephen Aylward w.r.t. the development of a registration toolkit to Slicer-3.	+	Introduction by Guido Gerig.
		+	Detailed presentation by Stephen Aylward w.r.t. the development of a registration toolkit to Slicer-3 (see status slides above), developed by the team Aylward/Ibanez/Turek.
		+
		+	A new 3D registration kit with rigid, affine and b-spline transformations is added to Slicer-3. Image match metrics include MattesMI (mutual information), normalized correlation and mean squared error, and three interpolators are available (linear, bspline, windowed sinc). A user has the choice of 3 optimization methods (gradient, multi-resolution with 3 levels, and 1+1 evolutionary followed by gradient).
		+
		+	The algorithm performs a series of steps from initial alignment over rigid, affine to BSpline. The image is finally transformed by cascading the set of transformations into one transformation step using the interpolation mode of choice.
		+
		+	Stephen Aylward showed a demonstration of a nonlinear registration of a subject-to-atlas matching with a case that showed difficulties using alternative registration techniques.
		+
		+	The BSpline mode uses a user-defined sampling of space. The current implementation does not yet allow for very dense sampling, e.g. every 2nd mm, as ultimately required for EMS to segment brain substructures at the finest resolution level. Further optimization of memory management and reducing #operations were discussed (Polina Golland's group and engineering core) and these Core-1 and Core-2 developers will get together to discuss options.