Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2021-01-01
|
| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/63455 |
| _version_ | 1811200969796485120 |
|---|---|
| author | Xuechun Wang Weilin Zeng Xiaodan Yang Yongsheng Zhang Chunyu Fang Shaoqun Zeng Yunyun Han Peng Fei |
| author_facet | Xuechun Wang Weilin Zeng Xiaodan Yang Yongsheng Zhang Chunyu Fang Shaoqun Zeng Yunyun Han Peng Fei |
| author_sort | Xuechun Wang |
| collection | DOAJ |
| description | We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, 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 the 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. |
| first_indexed | 2024-04-12T02:13:41Z |
| format | Article |
| id | doaj.art-b70899a9511f458cbdbed38cafbdfc8b |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:13:41Z |
| publishDate | 2021-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-b70899a9511f458cbdbed38cafbdfc8b2022-12-22T03:52:19ZengeLife Sciences Publications LtdeLife2050-084X2021-01-011010.7554/eLife.63455Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brainXuechun Wang0Weilin Zeng1Xiaodan Yang2Yongsheng Zhang3Chunyu Fang4Shaoqun Zeng5https://orcid.org/0000-0002-1802-337XYunyun Han6Peng Fei7https://orcid.org/0000-0003-3764-817XSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, ChinaWe have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, 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 the 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.https://elifesciences.org/articles/63455mouse braindeep-learningimage registration |
| spellingShingle | Xuechun Wang Weilin Zeng Xiaodan Yang Yongsheng Zhang Chunyu Fang Shaoqun Zeng Yunyun Han Peng Fei Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain eLife mouse brain deep-learning image registration |
| title | Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain |
| title_full | Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain |
| title_fullStr | Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain |
| title_full_unstemmed | Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain |
| title_short | Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain |
| title_sort | bi channel image registration and deep learning segmentation birds for efficient versatile 3d mapping of mouse brain |
| topic | mouse brain deep-learning image registration |
| url | https://elifesciences.org/articles/63455 |
| work_keys_str_mv | AT xuechunwang bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT weilinzeng bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT xiaodanyang bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT yongshengzhang bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT chunyufang bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT shaoqunzeng bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT yunyunhan bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain AT pengfei bichannelimageregistrationanddeeplearningsegmentationbirdsforefficientversatile3dmappingofmousebrain |