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MR-CT multi-atlas registration guided by fully automated brain structure segmentation with CNNs
Sina Walluscheck1, Luca Canalini2, Hannah Strohm2
1Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany. sina.walluscheck@mevis.fraunhofer.de.
International Journal of Computer Assisted Radiology and Surgery
|November 5, 2022
Summary
This study introduces an automated method to register MRI brain atlases to CT scans, enabling detailed anatomical analysis. Convolutional neural networks (CNNs) enhance this process by automatically segmenting brain structures for improved accuracy.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Radiology
Background:
- Computed tomography (CT) is crucial for identifying brain anomalies, but its soft tissue contrast limits detailed anatomical segmentation.
- Magnetic resonance imaging (MRI) is typically used for precise brain region division, posing challenges for CT-based diagnostics.
Purpose of the Study:
- To develop a fully automated method for registering MRI brain atlas data to CT scans.
- To enable detailed, automated anatomical reporting of brain CT examinations.
Main Methods:
- A multi-atlas registration approach was employed, propagating anatomical information from an MRI atlas to CT scans.
- Lateral ventricles and brain volume masks from CT images were used to guide the registration.
- Convolutional neural networks (CNNs) were validated for automated segmentation to enhance registration accuracy.
Main Results:
- The registration method achieved high accuracy, with mean Dice similarity coefficients of 0.92 (ventricles) and 0.99 (parenchyma) using manual segmentation guidance.
- Using CNN-based automated segmentation for guidance resulted in mean Dice values of 0.87 (ventricles) and 0.98 (parenchyma).
Conclusions:
- The proposed approach offers a fully automated solution for registering MRI atlases to CT scans, providing detailed anatomical insights.
- CNN-based segmentation effectively generates masks for brain ventricles and volume, significantly aiding the registration process.
