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Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no au...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665560/ https://www.ncbi.nlm.nih.gov/pubmed/37993550 http://dx.doi.org/10.1038/s41598-023-47070-3 |
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author | Vogel, Fabian W. Alipek, Sercan Eppler, Jens-Bastian Osuna-Vargas, Pamela Triesch, Jochen Bissen, Diane Acker-Palmer, Amparo Rumpel, Simon Kaschube, Matthias |
author_facet | Vogel, Fabian W. Alipek, Sercan Eppler, Jens-Bastian Osuna-Vargas, Pamela Triesch, Jochen Bissen, Diane Acker-Palmer, Amparo Rumpel, Simon Kaschube, Matthias |
author_sort | Vogel, Fabian W. |
collection | PubMed |
description | Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters. |
format | Online Article Text |
id | pubmed-10665560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106655602023-11-22 Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging Vogel, Fabian W. Alipek, Sercan Eppler, Jens-Bastian Osuna-Vargas, Pamela Triesch, Jochen Bissen, Diane Acker-Palmer, Amparo Rumpel, Simon Kaschube, Matthias Sci Rep Article Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters. Nature Publishing Group UK 2023-11-22 /pmc/articles/PMC10665560/ /pubmed/37993550 http://dx.doi.org/10.1038/s41598-023-47070-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vogel, Fabian W. Alipek, Sercan Eppler, Jens-Bastian Osuna-Vargas, Pamela Triesch, Jochen Bissen, Diane Acker-Palmer, Amparo Rumpel, Simon Kaschube, Matthias Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title | Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title_full | Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title_fullStr | Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title_full_unstemmed | Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title_short | Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging |
title_sort | utilizing 2d-region-based cnns for automatic dendritic spine detection in 3d live cell imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665560/ https://www.ncbi.nlm.nih.gov/pubmed/37993550 http://dx.doi.org/10.1038/s41598-023-47070-3 |
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