ArticleeLife2021
Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 24 citations in OpenAlex.
- Brain-wide 3D neuron detection and mapping with deep learning.Neurophotonics · 2025Article
- A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.Nature methods · 2025Article
- Regulating Integrin β1 to Restore Gonadotropin-Releasing Hormone-Tanycyte Unit Function in Polycystic Ovary Syndrome-Related Hypothalamic Dysregulation.Research (Washington, D.C.) · 2025Article
- eLemur: A cellular-resolution 3D atlas of the mouse lemur brain.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- SOLID: minimizing tissue distortion for brain-wide profiling of diverse architectures.Nature communications · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- Rapid 3D isotropic imaging of whole organ with double-ring light-sheet microscopy and self-learning side-lobe elimination.Biomedical optics express · 2023Article
- D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.Nature methods · 2023Article
- Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.Neuroinformatics · 2023Article
- Use of a tissue clearing technique combined with retrograde trans-synaptic viral tracing to evaluate changes in mouse retinorecipient brain regions following optic nerve crush.Neural regeneration research · 2023Article
- An interactive image segmentation method for the anatomical structures of the main olfactory bulb with micro-level resolution.Frontiers in neuroinformatics · 2023Article
- Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq andFrontiers in neuroanatomy · 2023Article
- Mouse embryo phenotyping using X-ray microCT.Frontiers in cell and developmental biology · 2022Review
- Progressive 3D biomedical image registration network based on deep self-calibration.Frontiers in neuroinformatics · 2022Article
- Tools for efficient analysis of neurons in a 3D reference atlas of whole mouse spinal cord.Cell reports methods · 2021Article
- Tissue clearing and 3D imaging in developmental biology.Development (Cambridge, England) · 2021Review
- Three-Dimensional X-ray Imaging of β-Galactosidase Reporter Activity by Micro-CT: Implication for Quantitative Analysis of Gene Expression.Brain sciences · 2021Article
- Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy.Nature communications · 2021Article
- TISSUE CLEARING.Nature reviews. Methods primers · 2021Article
- Automatic Segmentation and Cardiac Mechanics Analysis of Evolving Zebrafish Using Deep Learning.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.