1. Computational and Systems Biology
  2. Neuroscience

Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han  Is a corresponding author
  6. Peng Fei  Is a corresponding author
  1. Huazhong University of Science and Technology, China
  2. Tongji Medical College, Huazhong University of Science and Technology, China
https://doi.org/10.7554/eLife.63455
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  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han
  6. Peng Fei
(2021)
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
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  3. Download .RIS

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

The following previously published data sets were used

Article and author information

Author details

  1. Xuechun Wang

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  2. Weilin Zeng

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  3. Xiaodan Yang

    School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  4. Chunyu Fang

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  5. Yunyun Han

    School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    For correspondence
    yhan@hust.edu.cn
    Competing interests
    The authors declare that no competing interests exist.

  6. Peng Fei

    School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
    For correspondence
    feipeng@hust.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3764-817X

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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  1. Xuechun Wang
  2. Weilin Zeng
  3. Xiaodan Yang
  4. Chunyu Fang
  5. Yunyun Han
  6. Peng Fei
(2021)
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

Share this article

https://doi.org/10.7554/eLife.63455

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