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Effects of registration regularization and atlas sharpness on segmentation accuracy.
B T Thomas Yeo1, Mert R Sabuncu, Rahul Desikan
1Computer Science and Artificial Intelligence Lab, MIT, USA.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 7, 2007
Summary
This study introduces a unified framework for brain atlas creation and registration. Optimal brain parcellation is achieved by balancing atlas sharpness and registration flexibility.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Anatomy
Background:
- Non-rigid registration involves a trade-off between warp regularization and image fidelity, often set empirically.
- This trade-off influences the "sharpness" of probabilistic atlases derived from manually labeled data, ranging from sharp to blurry.
Purpose of the Study:
- To propose a unified framework for computing brain atlases at various sharpness levels.
- To enable joint registration-segmentation of new brain images with these atlases.
- To investigate the impact of atlas sharpness and warp regularization on cortical surface parcellation.
Main Methods:
- Developed a unified framework for atlas computation and joint registration-segmentation.
- Explored three strategies: progressive registration-segmentation to sharpening atlases, progressive registration to a single atlas, and registration with fixed warps.
- Evaluated the balance between atlas sharpness and warp regularization.
Main Results:
- Identified an optimal balance between atlas sharpness and warp regularization for parcellation.
- Demonstrated statistically significant improvements in parcellation results compared to previous methods.
- Showcased the framework's ability to produce probabilistic atlases of varying sharpness.
Conclusions:
- The proposed framework offers a principled approach to atlas-based brain image analysis.
- Optimizing the trade-off between atlas sharpness and registration flexibility is crucial for accurate cortical parcellation.
- This method advances the field of neuroimaging by improving registration and segmentation accuracy.
