Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice
Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for t...
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Bio-protocol LLC
2025-01-01
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| author | Ryszard Gomolka |
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| description | Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain. The algorithm employs adaptive thresholding and region growing to accurately and repeatably delineate CSF space regions in 3D constructive interference steady-state (3D-CISS) images acquired using a 9.4 Tesla MR system and a cryogenically cooled transmit/receive resonator. Key steps include computing a bounding box enclosing the brain parenchyma in three dimensions, applying an adaptive intensity threshold, and refining CSF regions independently in sagittal, axial, and coronal planes. In its original application, the algorithm provided objective and repeatable delineation of CSF regions in 3D-CISS images of sub-optimal signal-to-noise ratio, acquired with (33 μm)3 isometric voxel dimensions. It allowed revealing subtle differences in CSF volumes between aquaporin-4-null and wild-type littermate mice, showing robustness and reliability. Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. By adjusting parameters, the algorithm is flexible for application in segmenting other types of anatomical structures or other types of 3D images. This automated method significantly reduces the time and effort compared to manual segmentation and offers higher repeatability, making it a valuable tool for preclinical and potentially clinical MRI applications. |
| format | Article |
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| institution | Kabale University |
| issn | 2331-8325 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Bio-protocol LLC |
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| spelling | doaj-art-2de512709ac64ee7a442e5340098395a2025-02-07T08:16:31ZengBio-protocol LLCBio-Protocol2331-83252025-01-0115110.21769/BioProtoc.5148Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in MiceRyszard Gomolka0Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, DenmarkMagnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain. The algorithm employs adaptive thresholding and region growing to accurately and repeatably delineate CSF space regions in 3D constructive interference steady-state (3D-CISS) images acquired using a 9.4 Tesla MR system and a cryogenically cooled transmit/receive resonator. Key steps include computing a bounding box enclosing the brain parenchyma in three dimensions, applying an adaptive intensity threshold, and refining CSF regions independently in sagittal, axial, and coronal planes. In its original application, the algorithm provided objective and repeatable delineation of CSF regions in 3D-CISS images of sub-optimal signal-to-noise ratio, acquired with (33 μm)3 isometric voxel dimensions. It allowed revealing subtle differences in CSF volumes between aquaporin-4-null and wild-type littermate mice, showing robustness and reliability. Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. By adjusting parameters, the algorithm is flexible for application in segmenting other types of anatomical structures or other types of 3D images. This automated method significantly reduces the time and effort compared to manual segmentation and offers higher repeatability, making it a valuable tool for preclinical and potentially clinical MRI applications.https://bio-protocol.org/en/bpdetail?id=5148&type=0 |
| spellingShingle | Ryszard Gomolka Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice Bio-Protocol |
| title | Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice |
| title_full | Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice |
| title_fullStr | Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice |
| title_full_unstemmed | Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice |
| title_short | Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice |
| title_sort | automatic adaptive algorithm for delineation of cerebral spinal fluid regions for non contrast magnetic resonance imaging volumetry and cisternography in mice |
| url | https://bio-protocol.org/en/bpdetail?id=5148&type=0 |
| work_keys_str_mv | AT ryszardgomolka automaticadaptivealgorithmfordelineationofcerebralspinalfluidregionsfornoncontrastmagneticresonanceimagingvolumetryandcisternographyinmice |