D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging d...

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Veröffentlicht in:Nature methods Jg. 20; H. 10; S. 1593 - 1604
Hauptverfasser: Li, Zhongyu, Shang, Zengyi, Liu, Jingyi, Zhen, Haotian, Zhu, Entao, Zhong, Shilin, Sturgess, Robyn N., Zhou, Yitian, Hu, Xuemeng, Zhao, Xingyue, Wu, Yi, Li, Peiqi, Lin, Rui, Ren, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.10.2023
Nature Publishing Group
Schlagworte:
631/1647/245
631/378
Algorithms
Annotations
Automation
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Brain
Brain Mapping
Circuits
Computational neuroscience
Data analysis
Deep Learning
Fluorescence microscopy
Image Processing, Computer-Assisted - methods
Image segmentation
Laser microscopy
Learning
Life Sciences
Light sheets
Machine learning
Mapping
Mesoscale phenomena
Modules
Neural networks
Neuroimaging
Proteomics
Segmentation
Workflow
ISSN:1548-7091, 1548-7105, 1548-7105
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Zusammenfassung:Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested. D-LMBmap is a fully automated pipeline for mesoscale connectomics including deep-learning modules for axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap works accurately across cell types and modalities.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-023-01998-6