A probabilistic, non-parametric framework for inter-modality label fusion.

Area of Science:

• Medical Imaging
• Computational Anatomy
• Machine Learning

Background:

• Multi-atlas techniques are widely used for medical image segmentation, offering high performance and ease of use.
• Improving segmentation quality often involves locally weighting atlas contributions during label fusion.
• Defining these weights effectively in inter-modality scenarios, where image intensities differ, remains a challenge.

Purpose of the Study:

• To develop a principled label fusion scheme for inter-modality medical image segmentation.
• To propose a method that does not rely on voxel intensity consistency between atlases and the target image.
• To enhance the accuracy of brain MRI segmentation using a novel generative model.

Main Methods:

• A generative model was developed where each atlas intensity has an associated conditional distribution of target intensities.
• Segmentation was performed using variational expectation maximization (VEM) within a Bayesian framework.
• The proposed method addresses the challenge of inter-modality label fusion without requiring intensity normalization.

Main Results:

• The proposed label fusion algorithm demonstrated superior performance compared to traditional majority voting.
• The method outperformed a recently published inter-modality label fusion algorithm.
• Evaluation on proton density weighted brain MRI scans showed significant improvements in segmenting labeled structures.

Conclusions:

• The developed label fusion scheme offers a robust solution for inter-modality medical image segmentation.
• The generative model and VEM approach provide a principled way to define weights without intensity consistency.
• This technique advances multi-atlas segmentation, particularly in scenarios with diverse imaging modalities.