Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains
Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2020-07-01
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| Series: | Mathematical Morphology |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/mathm-2020-0100 |
| Summary: | Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible. |
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| ISSN: | 2353-3390 |