human age estimation with surface-based features from mri images
DESCRIPTION
Human Age Estimation with Surface-based Features from MRI Images. JOJO 2012.6.21. Outline. Background Methods Experiment & Results Conclusions. Background. Brain development pattern (BDP). Brain development. Disease. change. BDP (MRI image). Specific pattern. predict. change. - PowerPoint PPT PresentationTRANSCRIPT
Human Age Estimation with Surface-based Features from MRI Images
JOJO2012.6.21
Outline
Background
Methods
Experiment & Results
Conclusions
Background
Brain development
Normal aging process
Specific pattern
Brain development pattern (BDP)
Normal age
predict
Disease
BDP (MRI image)
Predicted age
change
change
↑ gap between true age
and predicted age
Background
Previous work:VBM --- GM/CSF changes with normal ageVBM --- predict age
no information about brain surface gyri and sulci
Surface-based features
Outline
Background
Methods
Experiment & Results
Conclusions
Methods (pipeline)
Methods (Surface-based)
1 single features:Cortical thickness
Mean curvature
Gaussian curvature
Methods (Surface-based)
2 Regional features:Desikan-killiany atlas (74 regions/hemisphere)
Cortical thicknessMean curvature Gaussian curvatureSurface area
Methods (Surface-based)
3 Brain network:Node ---- each ROI regionEdge ----
Methods (Surface-based)
4 Combined features:Mean curvature + Gaussian curvature2 Curv + Thick 2 Curv + Thick + surfArea
Outline
Background
Methods
Experiment & Results
Conclusions
Experiment & Results
Subjects chosen from IXI database
Num Males/females
Age mean ±SD (years) Age range
Subjects 360 175/185 47.04±16.16
20-82
Experiment & Results
Pipeline
Experiment & Results
Performance of different regional features
Experiment & Results
Performance of brain network
Experiment & Results
Performance of combined features
Experiment & Results
Visualization of results from the age estimation modelEach point in the figure represented an individual. Both values are highly correlated (corr=0.94). The blue line shows the value where predicted age matches real age.
Experiment & Results
Compare our model with previous work
Outline
Background
Methods
Experiment & Results
Conclusions
Conclusions
• Advantage1. Firstly apply surface-based features in age
estimation and analyze surface-based features performance from different angles.
2. Prediction results are the best one as far as we know.
Conclusions
• Disadvantage
Prediction accuracy is very sensitive to the subjects
Conclusions
• Future work1. Multi-modal data2. Combined with VBM3. Network 4. Apply to classify disease
Thank you!