Restoring morphology of light sheet microscopy data based on magnetic resonance histology
The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), whi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1011895/full |
| _version_ | 1797961511506804736 |
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| author | Yuqi Tian James J. Cook G. Allan Johnson |
| author_facet | Yuqi Tian James J. Cook G. Allan Johnson |
| author_sort | Yuqi Tian |
| collection | DOAJ |
| description | The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density. |
| first_indexed | 2024-04-11T00:59:17Z |
| format | Article |
| id | doaj.art-e35bdad2532d44b0b27a638751cea267 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T00:59:17Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-e35bdad2532d44b0b27a638751cea2672023-01-04T19:29:10ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-01-011610.3389/fnins.2022.10118951011895Restoring morphology of light sheet microscopy data based on magnetic resonance histologyYuqi TianJames J. CookG. Allan JohnsonThe combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.https://www.frontiersin.org/articles/10.3389/fnins.2022.1011895/fullmouse brain imagingmagnetic resonance histologylight sheet microscopycross-modality registrationtissue clearing |
| spellingShingle | Yuqi Tian James J. Cook G. Allan Johnson Restoring morphology of light sheet microscopy data based on magnetic resonance histology Frontiers in Neuroscience mouse brain imaging magnetic resonance histology light sheet microscopy cross-modality registration tissue clearing |
| title | Restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| title_full | Restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| title_fullStr | Restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| title_full_unstemmed | Restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| title_short | Restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| title_sort | restoring morphology of light sheet microscopy data based on magnetic resonance histology |
| topic | mouse brain imaging magnetic resonance histology light sheet microscopy cross-modality registration tissue clearing |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1011895/full |
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