ArticleNature methods2024
Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Whole-organism spatial transcriptomics at single-cell resolution inbioRxiv : the preprint server for biology · 2026Article
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Efficient pheromone navigation via antagonistic detectors in Caenorhabditis elegans male.Nature communications · 2026Article
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast.bioRxiv : the preprint server for biology · 2026Article
- Love-thy-neighbor: neural networks for tracking and lineage tracing in budding yeast.Bioinformatics advances · 2026Article
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- Automated computer vision and dose-response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform.Scientific reports · 2025Article
- Network modularity reveals context and state-dependent reorganization of time-varying functional connectivity in single-cell resolved neural activity recordings.bioRxiv : the preprint server for biology · 2025Article
- Integrative functional optical imaging approaches: Optics, microfluidics, and machine learning for neuroscience in organoids and small-animal models.Biophysical journal · 2025Review
- Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of anResearch square · 2025Article
- Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy.Brain informatics · 2024Article
- Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce 'targeted augmentation', a method to automatically synthesize artificial annotations from a few manual annotations. Our method ('Targettrack') learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.
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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.