Projects:GeodesicShapeRegression - Revision history

Jfishbaugh at 05:24, 14 October 2014

2014-10-14T05:24:13Z

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T05:11:23Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T05:07:23Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T05:07:03Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T05:02:15Z

Ongoing Work (Updated 10/2014)

Jfishbaugh at 04:51, 14 October 2014

2014-10-14T04:51:42Z

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T04:50:09Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T04:47:23Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T04:44:14Z

Ongoing Work (Updated 10/2014)

Jfishbaugh: /* Ongoing Work (Updated 10/2014) */

2014-10-14T04:43:41Z

Ongoing Work (Updated 10/2014)

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 __NOTOC__
-= Ongoing Work (Updated 10/2014) =
+= Ongoing Work =
 == Geodesic Regression for Anatomical Shape Complexes ==

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 However, even with the control point formulation, the dynamics of image evolution are likely characterized by considerably fewer parameters than the number of initial control points. We introduce a sparsity prior into model estimation. An upper bound on the dimensionality of the deformation is chosen by the user, and a L1 penalty selects the most relevant subset of initialmomenta, those that describe the most salient changes over time.
 [[File:Synth_geo_image_evo.png|600px|center|thumb|'''Fig. 8.''' Synthetic image evolution generated by shooting the baseline image (far left)
 along predefined initial momenta, constraining the resulting evolution to be geodesic.]]
 [[File:Synth_sparsity_panel.png|600px|center|thumb|'''Fig. 9.''' Baseline shape and initial momenta estimated for several values of sparsity
 parameter.. An increase in the sparsity parameter leads to a more compact representation, with momenta located in areas of dynamic change over time.]]
 [[File:Synth_sparsity_vs_datamatching.png|325px|center|thumb|'''Fig. 10.''' Impact of the sparsity parameter for the synthetic experiment . There is a range of values of the sparsity parameter which result in a considerable decrease in the number of control points for only minimal

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 parameter.. An increase in the sparsity parameter leads to a more compact representation, with momenta located in areas of dynamic change over time.]]
-[[File:Synth_sparsity_vs_datamatching.png|225px|center|thumb|'''Fig. 10.''' Impact of the sparsity parameter for the synthetic experiment . There is a range of values of the sparsity parameter which result in a considerable decrease in the number of control points for only minimal
+[[File:Synth_sparsity_vs_datamatching.png|325px|center|thumb|'''Fig. 10.''' Impact of the sparsity parameter for the synthetic experiment . There is a range of values of the sparsity parameter which result in a considerable decrease in the number of control points for only minimal
 increase in the relative error of the data matching term.]]

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 However, even with the control point formulation, the dynamics of image evolution are likely characterized by considerably fewer parameters than the number of initial control points. We introduce a sparsity prior into model estimation. An upper bound on the dimensionality of the deformation is chosen by the user, and a L1 penalty selects the most relevant subset of initialmomenta, those that describe the most salient changes over time.
-[[File:Synth_geo_image_evo.png|600px|center|thumb|'''Fig. 7.''' Synthetic image evolution generated by shooting the baseline image (far left)
+[[File:Synth_geo_image_evo.png|600px|center|thumb|'''Fig. 8.''' Synthetic image evolution generated by shooting the baseline image (far left)
 along predefined initial momenta, constraining the resulting evolution to be geodesic.]]
 = Literature =

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 healthy child observed at 6, 12, and 25 months of age. Regression on images alone is difficult in this case due to the very low contrast in the 6 month old image. Despite the low contrast, tissue segmentations can still be reliably and consistently estimated [10]. We estimate a geodesic model using only T1W images and a model jointly on images and white matter surfaces to emphasize the development of the tissue interface.
-The results of geodesic regression are shown for several snapshots in time in Fig. 3. The model estimated using only images mostly captures the scale change, but does not capture much deformation in the interior of the brain. The model estimated jointly on image and shape captures more detailed development as white matter stretches and expands.
+The results of geodesic regression are shown for several snapshots in time in Fig. 4. The model estimated using only images mostly captures the scale change, but does not capture much deformation in the interior of the brain. The model estimated jointly on image and shape captures more detailed development as white matter stretches and expands.
-[[File:Pediatric_image_vs_imageandshape_panel.png|600px|center|thumb|'''Fig. 3.''' Images and deformations estimated by geodesic regression
+[[File:Pediatric_image_vs_imageandshape_panel.png|600px|center|thumb|'''Fig. 4.''' Images and deformations estimated by geodesic regression
 using images alone (top) and jointly on images and white matter surfaces (bottom). Regression jointly on image and shape results in a more realistic evolution which captures detailed changes in brain tissue in addition to the increase in brain size. In both cases, geodesic regression was estimated backwards in time.]]
-The differences between the two models is most evident when viewed as a movie, shown in Fig. 4.
+The differences between the two models is most evident when viewed as a movie, shown in Fig. 5.
-[[File:Compare_image_vs_imageandshape2.gif|350px|center|thumb|'''Fig. 4.''' Animation of the models estimated with images only (left) and with images and white matter surfaces (right).]]
+[[File:Compare_image_vs_imageandshape2.gif|350px|center|thumb|'''Fig. 5.''' Animation of the models estimated with images only (left) and with images and white matter surfaces (right).]]
 '''Neurodegeneration in Huntington’s Disease'''
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 verified, and cleaned. Models are estimated using only T1W images as well as T1W images plus caudate surfaces.
-The trajectory of caudate volume extracted after regression is shown in Fig. 5. The model estimated from images alone fails to capture the volume loss observed in both caudates, and rather, shows an increase in right caudate volume. By incorporating caudate shape data in model estimation, we are able to capture the shrinking of the caudates. The corresponding expansion of the ventricles is also captured, shown
+The trajectory of caudate volume extracted after regression is shown in Fig. 6. The model estimated from images alone fails to capture the volume loss observed in both caudates, and rather, shows an increase in right caudate volume. By incorporating caudate shape data in model estimation, we are able to capture the shrinking of the caudates. The corresponding expansion of the ventricles is also captured, shown
 in Fig 4, due to the inclusion of imaging data. By incorporating shape and image information jointly, we are able to model both the expansion of the ventricles and the degeneration of the caudates. Accurate models of change are essential when extrapolating beyond the observation time interval, which can provide insight into disease progression.
-[[File:Hunt_image_vs_imageandshape.png|800px|center|thumb|'''Fig. 4.''' '''Top)''' Evolution estimated on images alone. Evolution of caudates are not estimated, but instead we shoot the baseline caudate shapes along the estimated geodesic. '''Bottom)''' Evolution estimated jointly using images and caudate shapes. Regression on images alone results in a slight expansion of ventricles, but does not capture the shrinking of caudates. By incorporating imaging and shape data, we capture both the expansion of ventricles and the shrinking of caudates.]]
+[[File:Hunt_image_vs_imageandshape.png|800px|center|thumb|'''Fig. 6.''' '''Top)''' Evolution estimated on images alone. Evolution of caudates are not estimated, but instead we shoot the baseline caudate shapes along the estimated geodesic. '''Bottom)''' Evolution estimated jointly using images and caudate shapes. Regression on images alone results in a slight expansion of ventricles, but does not capture the shrinking of caudates. By incorporating imaging and shape data, we capture both the expansion of ventricles and the shrinking of caudates.]]
-Fig. 5 shows an animation of model estimated for a single subject with Huntington's disease, showing interpolation inside the window of observed data as well as extrapolation 10 years from the baseline observation.
+Fig. 7 shows an animation of model estimated for a single subject with Huntington's disease, showing interpolation inside the window of observed data as well as extrapolation 10 years from the baseline observation.
-[[File:Hd_subj_50073_optim.gif|400px|center|thumb|'''Fig. 5.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]
+[[File:Hd_subj_50073_optim.gif|400px|center|thumb|'''Fig. 7.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]
 = Literature =

← Older revision		Revision as of 04:51, 14 October 2014
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	[[File:Hunt_image_vs_imageandshape.png\|800px\|center\|thumb\|'''Fig. 4.''' '''Top)''' Evolution estimated on images alone. Evolution of caudates are not estimated, but instead we shoot the baseline caudate shapes along the estimated geodesic. '''Bottom)''' Evolution estimated jointly using images and caudate shapes. Regression on images alone results in a slight expansion of ventricles, but does not capture the shrinking of caudates. By incorporating imaging and shape data, we capture both the expansion of ventricles and the shrinking of caudates.]]		[[File:Hunt_image_vs_imageandshape.png\|800px\|center\|thumb\|'''Fig. 4.''' '''Top)''' Evolution estimated on images alone. Evolution of caudates are not estimated, but instead we shoot the baseline caudate shapes along the estimated geodesic. '''Bottom)''' Evolution estimated jointly using images and caudate shapes. Regression on images alone results in a slight expansion of ventricles, but does not capture the shrinking of caudates. By incorporating imaging and shape data, we capture both the expansion of ventricles and the shrinking of caudates.]]
		+
		+	Fig. 5 shows an animation of model estimated for a single subject with Huntington's disease, showing interpolation inside the window of observed data as well as extrapolation 10 years from the baseline observation.

	[[File:Hd_subj_50073_optim.gif\|400px\|center\|thumb\|'''Fig. 5.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]		[[File:Hd_subj_50073_optim.gif\|400px\|center\|thumb\|'''Fig. 5.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]

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 [[File:Hunt_image_vs_imageandshape.png|800px|center|thumb|'''Fig. 4.''' '''Top)''' Evolution estimated on images alone. Evolution of caudates are not estimated, but instead we shoot the baseline caudate shapes along the estimated geodesic. '''Bottom)''' Evolution estimated jointly using images and caudate shapes. Regression on images alone results in a slight expansion of ventricles, but does not capture the shrinking of caudates. By incorporating imaging and shape data, we capture both the expansion of ventricles and the shrinking of caudates.]]
-[[File:Hd_subj_50073.gif|200px|center|thumb|'''Fig. 5.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]
+[[File:Hd_subj_50073_optim.gif|200px|center|thumb|'''Fig. 5.''' Animation of a model estimated with image and shape data. A green border around the image represents interpolation within the observation interval (3 years). A red border represents extrapolation out to 10 years from baseline.]]
 = Literature =