Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
- Huazhong University of Science and Technology, China
- Tongji Medical College, Huazhong University of Science and Technology, China
Abstract
We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.
Data availability
The Allen CCF is open access and available with related tools at https://atlas.brain-map.org/The datasets (Brain1~5) have been deposited in Dryad at https://datadryad.org/stash/share/4fesXcJif0L2DnSj7YmjREe37yPm1bEnUiK49ELtALgThe code and plugin can be found at the following link：https://github.com/bleach1by1/BIRDS_pluginhttps://github.com/bleach1by1/birds_reghttps://github.com/bleach1by1/birds_dl.githttps://github.com/bleach1by1/BIRDS_demoAll data generated or analysed during this study are included in the manuscript. Source data files have been provided for Figures 1, 2, 3, 4, 5 and Figure 2-figure supplement 3,4; Figure 5-figure supplement 2,3
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Brain 1 and 2Dryad Digital Repository.
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Brain 5Dryad Digital Repository.
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Brain 3 and 4Dryad Digital Repository.
Article and author information
Author details
Funding
National Key R&D program of China (2017YFA0700501)
- Peng Fei
National Natural Science Foundation of China (21874052)
- Peng Fei
National Natural Science Foundation of China (31871089)
- Yunyun Han
Innovation Fund of WNLO
- Peng Fei
Junior Thousand Talents Program of China
- Peng Fei
Junior Thousand Talents Program of China
- Yunyun Han
The FRFCU (HUST:2172019kfyXKJC077)
- Yunyun Han
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Wang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
eLife 10:e63455.
https://doi.org/10.7554/eLife.63455
Further reading
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- Computational and Systems Biology
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