{"ID":2867107,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18926","arxiv_id":"2509.18926","title":"SynapFlow: A Modular Framework Towards Large-Scale Analysis of Dendritic Spines","abstract":"Dendritic spines are key structural components of excitatory synapses in the brain. Given the size of dendritic spines provides a proxy for synaptic efficacy, their detection and tracking across time is important for studies of the neural basis of learning and memory. Despite their relevance, large-scale analyses of the structural dynamics of dendritic spines in 3D+time microscopy data remain challenging and labor-intense. Here, we present a modular machine learning-based pipeline designed to automate the detection, time-tracking, and feature extraction of dendritic spines in volumes chronically recorded with two-photon microscopy. Our approach tackles the challenges posed by biological data by combining a transformer-based detection module, a depth-tracking component that integrates spatial features, a time-tracking module to associate 3D spines across time by leveraging spatial consistency, and a feature extraction unit that quantifies biologically relevant spine properties. We validate our method on open-source labeled spine data, and on two complementary annotated datasets that we publish alongside this work: one for detection and depth-tracking, and one for time-tracking, which, to the best of our knowledge, is the first data of this kind. To encourage future research, we release our data, code, and pre-trained weights at https://github.com/pamelaosuna/SynapFlow, establishing a baseline for scalable, end-to-end analysis of dendritic spine dynamics.","short_abstract":"Dendritic spines are key structural components of excitatory synapses in the brain. Given the size of dendritic spines provides a proxy for synaptic efficacy, their detection and tracking across time is important for studies of the neural basis of learning and memory. Despite their relevance, large-scale analyses of th...","url_abs":"https://arxiv.org/abs/2509.18926","url_pdf":"https://arxiv.org/pdf/2509.18926v1","authors":"[\"Pamela Osuna-Vargas\",\"Altug Kamacioglu\",\"Dominik F. Aschauer\",\"Petros E. Vlachos\",\"Sercan Alipek\",\"Jochen Triesch\",\"Simon Rumpel\",\"Matthias Kaschube\"]","published":"2025-09-23T12:47:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":609440,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867107,"paper_url":"https://arxiv.org/abs/2509.18926","paper_title":"SynapFlow: A Modular Framework Towards Large-Scale Analysis of Dendritic Spines","repo_url":"https://github.com/pamelaosuna/SynapFlow","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}