ArticleCell reports methods2025
A deep learning pipeline for accurate and automated restoration, segmentation, and quantification of dendritic spines.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep learning-assisted mapping of dendritic spines using sequential 2D two-photon calcium imaging.iScience · 2026Article
- Curvature-based machine-learning method for automated segmentation of dendritic spines.Biophysical journal · 2026Article
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.PLoS computational biology · 2026Article
- Psychedelics relax predictive processing in the post-acute period by remodeling cortico-cortical feedback circuits.bioRxiv : the preprint server for biology · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.bioRxiv : the preprint server for biology · 2026Article
- CURVATURE-BASED MACHINE LEARNING METHOD FOR AUTOMATED SEGMENTATION OF DENDRITIC SPINES.bioRxiv : the preprint server for biology · 2025Article
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6 authors.
Funding
Abstract
Quantification of dendritic spines is essential for studying synaptic connectivity, yet most current approaches require manual adjustments or the combination of multiple software tools for optimal results. Here, we present restoration enhanced spine and neuron analysis (RESPAN), an open-source pipeline integrating state-of-the-art deep learning for image restoration, segmentation, and analysis in an easily deployable, user-friendly interface. Leveraging content-aware restoration to enhance signal, contrast, and isotropic resolution further enhances RESPAN's robust detection of spines, dendritic branches, and soma across a wide variety of samples, including challenging datasets with limited signal, such as rapid volumetric imaging and in vivo two-photon microscopy. Extensive validation against expert annotations and comparison with other software demonstrate RESPAN's superior accuracy and reproducibility across multiple imaging modalities. RESPAN offers significant improvements in usability over currently available approaches, streamlining and democratizing access to a combination of advanced capabilities through an accessible resource for the neuroscience community.
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Registered trials
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