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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.18926 |
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| _version_ | 1866916964445716480 |
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| author | Osuna-Vargas, Pamela Kamacioglu, Altug Aschauer, Dominik F. Vlachos, Petros E. Alipek, Sercan Triesch, Jochen Rumpel, Simon Kaschube, Matthias |
| author_facet | Osuna-Vargas, Pamela Kamacioglu, Altug Aschauer, Dominik F. Vlachos, Petros E. Alipek, Sercan Triesch, Jochen Rumpel, Simon Kaschube, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynapFlow: A Modular Framework Towards Large-Scale Analysis of Dendritic Spines Osuna-Vargas, Pamela Kamacioglu, Altug Aschauer, Dominik F. Vlachos, Petros E. Alipek, Sercan Triesch, Jochen Rumpel, Simon Kaschube, Matthias Computer Vision and Pattern Recognition 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. |
| title | SynapFlow: A Modular Framework Towards Large-Scale Analysis of Dendritic Spines |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.18926 |