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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
[Non-linear registration of MR brain images integrated with machine learning]
1Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, 200030.
Summary
This study introduces a machine learning approach for selecting optimal geometric features in deformable brain registration. This method enhances registration accuracy by approximately 10% for simulated deformations and improves real MR brain image registration, particularly in cortical regions.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Neuroscience
Context:
- Deformable brain registration is crucial for analyzing structural changes in neuroimaging studies.
- Accurate registration is essential for comparing brain images across subjects and time points.
- Current registration methods may struggle with localized anatomical variations.
Purpose:
- To develop a machine learning method for selecting optimal geometric features for deformable brain registration.
- To improve the accuracy and robustness of the HAMMER registration algorithm.
- To enhance the registration of complex brain structures, especially in cortical regions.
Summary:
- A novel machine learning technique identifies the most effective geometric features for deformable brain registration on a location-specific basis.
- These learned features are integrated into the HAMMER registration framework, leading to a ~10% increase in accuracy for simulated deformation fields.
- Significant improvements in registration quality were observed on real magnetic resonance (MR) brain images, particularly within the cerebral cortex.
Impact:
- This work advances the field of medical image analysis by providing a more accurate and adaptable brain registration tool.
- The enhanced registration accuracy can lead to more reliable diagnoses and treatment monitoring in neurological disorders.
- Improved registration of cortical regions facilitates detailed studies of brain morphology and function.
