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Human Age Estimation with Surface-based Features from MRI Images

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.

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Human Age Estimation with Surface-based Features from MRI Images

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  1. Human Age Estimation with Surface-based Features from MRI Images JOJO 2012.6.21

  2. Outline • Background • Methods • Experiment & Results • Conclusions

  3. Background Brain development pattern (BDP) Brain development Disease change BDP (MRI image) Specific pattern predict change ↑gap between true age and predicted age Predicted age Normal aging process Normal age

  4. Background Previous work: • VBM --- GM/CSF changes with normal age • VBM --- predict age Surface-based features no information about brain surface gyri and sulci

  5. Outline • Background • Methods • Experiment & Results • Conclusions

  6. Methods (pipeline)

  7. Methods (Surface-based) 1 single features: Cortical thickness Mean curvature Gaussian curvature

  8. Methods (Surface-based) 2 Regional features: Desikan-killiany atlas (74 regions/hemisphere) Cortical thickness Mean curvature Gaussian curvature Surface area

  9. Methods (Surface-based) 3 Brain network: Node ---- each ROI region Edge ----

  10. Methods (Surface-based) 4 Combined features: Mean curvature + Gaussian curvature 2 Curv + Thick 2 Curv + Thick + surfArea

  11. Outline • Background • Methods • Experiment & Results • Conclusions

  12. Experiment & Results Subjects chosen from IXI database

  13. Experiment & Results Pipeline

  14. Experiment & Results Performance of different regional features

  15. Experiment & Results Performance of brain network

  16. Experiment & Results Performance of combined features

  17. Experiment & Results

  18. Visualization of results from the age estimation model Each 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.

  19. Experiment & Results Compare our model with previous work

  20. Outline • Background • Methods • Experiment & Results • Conclusions

  21. Conclusions • Advantage • Firstly apply surface-based features in age estimation and analyze surface-based features performance from different angles. • Prediction results are the best one as far as we know.

  22. Conclusions • Disadvantage Prediction accuracy is very sensitive to the subjects

  23. Conclusions • Future work • Multi-modal data • Combined with VBM • Network • Apply to classify disease

  24. Thank you!

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