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Computer Vision Evidence Supporting Craniometric Alignment of Rat Brain Atlases to Streamline Expert-Guided,
Arshad M Khan1,2,3,4, Jose G Perez3,5, Claire E Wells1,2,6
1UTEP Systems Neuroscience Laboratory, University of Texas at El Paso El Paso, TX, United States.
Frontiers in Systems Neuroscience
|May 17, 2018
Summary
This study introduces a tool for aligning rat brain atlases, enabling neuroanatomical data migration between Paxinos and Watson (PW) and Swanson (S) reference spaces. This facilitates accurate contextualization of data across different atlases.
Area of Science:
- Neuroscience
- Neuroanatomy
- Computational Neuroscience
Background:
- The rat brain is extensively studied, with multiple reference atlases like Paxinos and Watson (PW) and Swanson (S) available.
- Existing atlases, despite decades of publication, lack a standardized framework for inter-atlas data alignment.
- This hinders the accurate contextualization of neuroanatomical data mapped in one atlas with data from another.
Purpose of the Study:
- To develop and present a tool for aligning rat brain atlas levels between the Paxinos and Watson (PW) and Swanson (S) reference spaces.
- To enable accurate data migration and contextualization of neuroanatomical data across these distinct atlas frameworks.
- To provide a foundational method for integrating datasets from different spatial reference systems for the rat brain.
Main Methods:
- Alignment of atlas levels based on the anteroposterior stereotaxic coordinate (z-axis) relative to Bregma (β).
- Utilized one-dimensional Cleveland dot plots for z-axis alignment validation.
- Employed a computer vision application with Scale-Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC) for region comparison.
- Demonstrated data migration using anisotropic scaling of vector-formatted atlas templates and expert-guided outlier correction.
Main Results:
- Successfully aligned atlas levels from multiple editions of PW and S reference spaces along the z-axis.
- Validated alignment strategies through agreement between Cleveland dot plots and computer vision methods.
- Showcased first-order approximation of point source data (hypothalamic microinjection sites) migration from PW to S space.
- Demonstrated that migrated data can be contextualized with existing datasets in S space for hypothesis generation.
Conclusions:
- The developed tool provides a basic framework for user-guided, first-order data migration between PW and S rat brain atlases.
- This facilitates the integration and contextualization of neuroanatomical data across different reference systems.
- The alignment strategies are potentially extensible to other spatial reference systems for the rat brain.
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