RESPAN: A Deep Learning Pipeline for Accurate and Automated Restoration, Segmentation, and Quantification of Dendritic Spines

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 RESPANs robust detection of spines, dendritic branches, and soma across a wide variety of samples, including challenging datasets such as those from live imaging and in vivo 2-photon microscopy with limited signal. Extensive validation against expert annotations and comparison with other software demonstrates RESPANs 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. MOTIVATIONAccurate and unbiased reconstructions of neuronal morphology and quantification of dendritic spines are widely used in neuroscience but remain a significant challenge for efficient large-scale analysis. Current methods rely heavily on parameter optimization between images and manual annotation, introducing bias and creating bottlenecks that limit large-scale studies. Additionally, existing automated tools often require complex workflows across multiple software platforms and lack integrated validation capabilities. We developed RESPAN to address these limitations by providing a comprehensive, automated pipeline that combines state-of-the-art deep learning approaches for image restoration, model training, image segmentation and analysis within one user-friendly graphic interface. This enables rapid, unbiased analysis of dendritic spine morphology across diverse imaging modalities while maintaining high accuracy and reproducibility.