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A semi-automatic image segmentation method for extraction of brain volume from in vivo mouse head magnetic resonance
Mariano G Uberti1, Michael D Boska, Yutong Liu
1Center for Neurovirology and Neurodegenerative Disorders, University of Nebraska Medical Center, Omaha, NE 68198-5880, USA.
Abstract:
In vivo magnetic resonance imaging (MRI) of mouse brain has been widely used to non-invasively monitor disease progression and/or therapeutic effects in murine models of human neurodegenerative disease. Segmentation of MRI to differentiate brain from non-brain tissue (usually referred to as brain extraction) is required for many MRI data processing and analysis methods, including coregistration, statistical parametric analysis, and mapping to brain atlas and histology. This paper presents a semi-automatic brain extraction technique based on a level set method with the incorporation of user-defined constraints. The constraints are derived from the prior knowledge of brain anatomy by defining brain boundary on orthogonal planes of the MRI. Constraints are incorporated in the level set method by spatially varying the weighting factors of the internal and external forces and modifying the image gradient (edge) map. Both two-dimensional multislice and three-dimensional versions of the brain extraction technique were developed and applied to MRI data with minimal brain/non-brain contrast T(1)-weighted (T(1)-wt) FLASH and maximized contrast T(2)-weighted (T(2)-wt) RARE. Results were evaluated by calculating the overlap measure (OM) between the automatically segmented and manually traced brain volumes. Results demonstrate that this technique accurately extracts the brain volume (mean OM=94%) and consistently outperformed the region growing method applied to the T(2)-wt RARE MRI (mean OM=81%). This method not only successfully extracts the mouse brain in low and high contrast MRI, but can also be used to segment other organs and tissues.
Insights
This study introduces a semi-automatic method for extracting mouse brains from MRI scans. The novel technique accurately segments brain tissue, outperforming existing methods for neurodegenerative disease research.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Biomedical Engineering
Background:
- In vivo magnetic resonance imaging (MRI) is crucial for monitoring neurodegenerative diseases in mouse models.
- Accurate brain extraction from MRI is essential for subsequent analysis, including registration and atlas mapping.
Purpose of the Study:
- To develop and evaluate a semi-automatic brain extraction technique for mouse MRI.
- To improve the accuracy and robustness of brain segmentation in low and high contrast MRI data.
Main Methods:
- A semi-automatic brain extraction technique utilizing a level set method with user-defined anatomical constraints.
- Incorporation of constraints by modifying internal/external forces and image gradient maps.
- Development of both 2D multislice and 3D versions, tested on T(1)-weighted and T(2)-weighted MRI.
Main Results:
- The technique achieved high accuracy in brain extraction, with a mean overlap measure (OM) of 94%.
- Outperformed the region growing method (mean OM=81%) on T(2)-weighted RARE MRI.
- Demonstrated successful segmentation across varying contrast levels and potential for segmenting other tissues.
Conclusions:
- The developed semi-automatic level set method provides accurate and robust mouse brain extraction from MRI.
- This technique offers a significant improvement over existing methods, particularly for low-contrast images.
- The method shows promise for broader applications in biological tissue segmentation.
