Projects:LongitudinalShapeAnalysis - Revision history

Jfishbaugh at 05:39, 14 October 2014

2014-10-14T05:39:57Z

Jfishbaugh at 05:28, 14 October 2014

2014-10-14T05:28:52Z

Jfishbaugh at 05:23, 14 October 2014

2014-10-14T05:23:34Z

Jfishbaugh at 05:22, 14 October 2014

2014-10-14T05:22:54Z

Jfishbaugh: /* Ongoing Work (Updated 8/14/12) */

2014-10-14T05:22:04Z

Ongoing Work (Updated 8/14/12)

Jfishbaugh at 05:21, 14 October 2014

2014-10-14T05:21:44Z

Jfishbaugh at 18:07, 15 August 2012

2012-08-15T18:07:57Z

Jfishbaugh at 18:07, 15 August 2012

2012-08-15T18:07:33Z

Jfishbaugh at 18:05, 15 August 2012

2012-08-15T18:05:28Z

Jfishbaugh at 18:02, 15 August 2012

2012-08-15T18:02:33Z

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 the need to incorporate biostatistical modeling, that combines patient variables
 with our computational analysis.
 = Literature =

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 with our computational analysis.
-=== Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics ===
+== Diffeomorphic Shape Trajectories for Improved Longitudinal Segmentation and Statistics ==
 Longitudinal image data has several sources of variability. First, there is inherent biological variability, both within a subject changing over time and also between subjects in a population. The goal of longitudinal analysis is to quantify this variability and make inferences about changes over time in a population. However, longitudinal imaging data also include unwanted sources of variability, such as noise in image acquisition, segmentation and registration errors, and human expert judgment, among others. These extraneous errors tend to dampen statistical power, especially when trying to distinguish between trajectories of two different populations, e.g., healthy and diseased.
 [[File:Caudate_volume_plot.png|800px|center|thumb|'''Fig. 6.''' Left: For one subject, volume of observed caudates (open circles) and temporally consistent continuous volume extracted from diffeomorphic shape model (solid line). The difference in caudate volume extracted from scans obtained on the same day highlights the need for consistent segmentation. Right: Observed volume and volume extracted from diffeomorphic shape models for all 65 subjects. While the volume of the discrete shape observations show considerable variation, volume extracted from personalized models are continuous and temporally consistent.]]
 = Literature =

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 __NOTOC__
-= Ongoing Work (Updated 10/14) =
+= Ongoing Work =
 == Analysis of Longitudinal Shape Variability ==

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 __NOTOC__
-= Ongoing Work (Updated 8/14/12) =
+= Ongoing Work (Updated 10/14) =
 == Analysis of Longitudinal Shape Variability ==

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 Longitudinal image data has several sources of variability. First, there is inherent biological variability, both within a subject changing over time and also between subjects in a population. The goal of longitudinal analysis is to quantify this variability and make inferences about changes over time in a population. However, longitudinal imaging data also include unwanted sources of variability, such as noise in image acquisition, segmentation and registration errors, and human expert judgment, among others. These extraneous errors tend to dampen statistical power, especially when trying to distinguish between trajectories of two different populations, e.g., healthy and diseased.
-[[File:Caudate_volume_plot.png|600px|center|thumb|'''Fig. 6.''' Left: For one subject, volume of observed caudates (open circles) and temporally consistent continuous volume extracted from diffeomorphic shape model (solid line). The difference in caudate volume extracted from scans obtained on the same day highlights the need for consistent segmentation. Right: Observed volume and volume extracted from diffeomorphic shape models for all 65 subjects. While the volume of the discrete shape observations show considerable variation, volume extracted from personalized models are continuous and temporally consistent.]]
+[[File:Caudate_volume_plot.png|800px|center|thumb|'''Fig. 6.''' Left: For one subject, volume of observed caudates (open circles) and temporally consistent continuous volume extracted from diffeomorphic shape model (solid line). The difference in caudate volume extracted from scans obtained on the same day highlights the need for consistent segmentation. Right: Observed volume and volume extracted from diffeomorphic shape models for all 65 subjects. While the volume of the discrete shape observations show considerable variation, volume extracted from personalized models are continuous and temporally consistent.]]
 = Literature =

@@ Line 73: / Line 73: @@
 This could be an interesting avenue to pursue for future research.
-[[File:Ace_modes_final.png|600px|center|thumb|'''Fig. 5.''' The first mode from PCA (mean plus one standard deviation) at selected time points for the autism database. Color indicates displacement from the mean shape.]]
+[[File:Ace_modes_final.png|700px|center|thumb|'''Fig. 5.''' The first mode from PCA (mean plus one standard deviation) at selected time points for the autism database. Color indicates displacement from the mean shape.]]
 Hypothesis testing is conducted on the magnitude of initial momenta between

@@ Line 53: / Line 53: @@
 [[File:Synth_pvalue.png|600px|center|thumb|'''Fig. 4.''' Significant differences in magnitude of momenta between group A and B at several time points, with p-values displayed on the surface of the reference atlas.]]
 = Literature =
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 [1] Fishbaugh, J., Durrleman, S., Gerig, G.: Estimation of smooth growth trajectories with controlled acceleration from time series shape data. In: Fichtinger, G., Peters, T. (eds.) MICCAI. LNCS, vol. 6892, pp. 401–408. Springer (2011)
-[2] Vaillant, M., Glaun`es, J.: Surface matching via currents. In: IPMI. LNCS, vol. 3565, pp. 381–392. Springer (2005)
+[2] Vaillant, M., Glaunes, J.: Surface matching via currents. In: IPMI. LNCS, vol. 3565, pp. 381–392. Springer (2005)
-[3] Miller, M, I., Trouv´e, A., Younes, L.: On the metrics and Euler-Lagrange equations of Computational Anatomy. Annual Review of Biomedical Engineering 4, 375–405 (2002)
+[3] Miller, M, I., Trouve, A., Younes, L.: On the metrics and Euler-Lagrange equations of Computational Anatomy. Annual Review of Biomedical Engineering 4, 375–405 (2002)

@@ Line 49: / Line 49: @@
 shows the Bonferroni corrected p-values shown on the reference atlas at selected
 time points. We observe significance on the left and right side of the shapes at
-, 12, and 16 months, corresponding to the bulge growing in opposite directions
+, 12, and 16 months, corresponding to the bulge growing in opposite directions in group A and B. It is also important to note that we observe no significant
 = Literature =

← Older revision		Revision as of 18:02, 15 August 2012
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	[[File:Synth_example_database_medium.png\|600px\|center\|thumb\|'''Fig. 2.''' The synthetic shape database with observations at 6, 10, 12, 18, and 24 months. '''Top:''' Typical shape observations for a subject from group A. '''Middle:''' The normative growth scenario. '''Bottom:''' Typical shape observations for a subject from group B.]]		[[File:Synth_example_database_medium.png\|600px\|center\|thumb\|'''Fig. 2.''' The synthetic shape database with observations at 6, 10, 12, 18, and 24 months. '''Top:''' Typical shape observations for a subject from group A. '''Middle:''' The normative growth scenario. '''Bottom:''' Typical shape observations for a subject from group B.]]
		+
		+	First, we perform PCA on the momenta that warp the normative atlas to
		+	each individual in group A. The first major mode of variation is summarized in
		+	Fig. 3 for several time points. This mode explains the variability in group A with
		+	respect to the reference shapes. The bulge on the left side of the shape is clearly
		+	identified along with variability in scale. A PCA on group B produces similar
		+	results, however it captures the bulge on the right side of the shape.
		+
		+	[[File:Synth_mode_deformation.png\|600px\|center\|thumb\|'''Fig. 3.''' The first major mode of deformation from PCA (mean plus one standard deviation) at selected time points for group A. Color indicates the displacement from the mean shape. The variability in the protuberance is clearly captured.]]
		+
		+	We also conduct hypothesis testing to determine if there are significant differences
		+	between group A and B. For each shape point, an independent t-test
		+	is performed on the magnitude of initial momenta which parameterize the mapping
		+	from reference atlas to individuals. We are testing if the distribution of
		+	momenta magnitude at each shape point is different between each group. Fig. 4
		+	shows the Bonferroni corrected p-values shown on the reference atlas at selected
		+	time points. We observe significance on the left and right side of the shapes at
		+	9, 12, and 16 months, corresponding to the bulge growing in opposite directions

	= Literature =		= Literature =