ArticleProceedings of the National Academy of Sciences of the United States of America2019
Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- NeuroSeg-MF: robust neuron segmentation in two-photon CaBiomedical optics express · 2026Article
- Combinatorial responsiveness of chemosensory neurons in mouse explants revealed by DynamicNeuroTracker.Cell reports methods · 2025Article
- AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.Annals of neurology · 2025Review
- CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.eNeuro · 2025Article
- A cell-type-specific epigenetic mechanism encodes social investigatory behavior via Lrhcn1 and Hcn1.Science advances · 2025Article
- Imaging neuronal voltage beyond the scattering limit.Nature methods · 2025Article
- Article
- Label-free metabolic fingerprinting of motile mammalian spermatozoa with subcellular resolution.BMC biology · 2025Article
- Window into the Brain: In Vivo Multiphoton Imaging.ACS photonics · 2025Review
- CalTrig: A GUI-based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.bioRxiv : the preprint server for biology · 2024Article
- An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data.Nature machine intelligence · 2024Article
- Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering.Communications biology · 2024Article
- Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations.IBRO neuroscience reports · 2024Article
- NeuroSeg-III: efficient neuron segmentation in two-photon CaBiomedical optics express · 2024Article
- A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels.eNeuro · 2024Article
- A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds.Bioengineering (Basel, Switzerland) · 2024Article
- Review
- Aerobic glycolysis is the predominant means of glucose metabolism in neuronal somata, which protects against oxidative damage.Nature neuroscience · 2023Article
- Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.Neurophotonics · 2023Article
- Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.
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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.