A deep learning pipeline for accurate and automated restoration, segmentation, and quantification of dendritic spines.

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 agai

SUBMITTER: Bernal-Garcia S 

PROVIDER: S-EPMC12570329 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature