ArticleNature methods2023
D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- System-level synchronization and subtype-specific modules govern brain-wide serotonin axon innervation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images.bioRxiv : the preprint server for biology · 2026Article
- DMC-BrainMap is an open-source, end-to-end tool for multi-feature brain mapping in different species.Cell reports methods · 2026Article
- Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy.bioRxiv : the preprint server for biology · 2025Article
- Delivery of a fibrin-binding hemostatic polymer ameliorates neurovascular damage and neural tissue loss after traumatic brain injury.Science advances · 2025Article
- Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction.Biomedical optics express · 2025Article
- Brain-wide 3D neuron detection and mapping with deep learning.Neurophotonics · 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
- Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders.Molecular psychiatry · 2024Review
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- NIEND: neuronal image enhancement through noise disentanglement.Bioinformatics (Oxford, England) · 2024Article
- Article
- Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.bioRxiv : the preprint server for biology · 2023Article
- An interactive image segmentation method for the anatomical structures of the main olfactory bulb with micro-level resolution.Frontiers in neuroinformatics · 2023Article
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Authors and funding
14 authors.
Funding
Abstract
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.
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