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Comparison of shape regression methods under landmark position uncertainty.

Nora Baka¹, Coert Metz, Michiel Schaap

¹Erasmus MC, Rotterdam, The Netherlands. n.baka@erasmusmc.nl

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 15, 2011

Summary

This study compares linear regression methods for shape estimation, focusing on landmark uncertainties. Including test shape uncertainty in regression improved results when training and test data uncertainties differed.

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Area of Science:

Medical imaging analysis
Statistical shape modeling

Background:

Regression-based shape estimation is gaining interest.
No systematic comparison of regression methods for shape estimation exists, especially considering landmark uncertainties.

Purpose of the Study:

To systematically compare linear regression methods for shape estimation.
To investigate the impact of landmark position uncertainties in training and test datasets.
To evaluate performance under varying uncertainty scenarios.

Main Methods:

Comparison of linear regression methods for shape estimation.
Investigation of two scenarios: similar and different landmark uncertainties between training and test data.
Testing on simulated data and statistical models of the left ventricle and femur.

Main Results:

Linear regression methods performed similarly when landmark uncertainties were consistent between training and test data.
Incorporating estimates of test shape landmark uncertainty into the regression improved performance when uncertainties differed.
Performance varied based on the specific dataset and uncertainty characteristics.

Conclusions:

The choice of regression method and handling of landmark uncertainties are crucial for accurate shape estimation.
Accounting for differing landmark uncertainties between training and test data is essential for improving regression-based shape estimation models.
Future research should explore advanced regression techniques and robust uncertainty quantification.