An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images
Abstract Background In the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefore, they have always been a major focus of neuroscience research. Two-p...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2017-12-01
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| Series: | BioData Mining |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13040-017-0161-5 |
| _version_ | 1818792137395798016 |
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| author | Qiwei Xie Xi Chen Hao Deng Danqian Liu Yingyu Sun Xiaojuan Zhou Yang Yang Hua Han |
| author_facet | Qiwei Xie Xi Chen Hao Deng Danqian Liu Yingyu Sun Xiaojuan Zhou Yang Yang Hua Han |
| author_sort | Qiwei Xie |
| collection | DOAJ |
| description | Abstract Background In the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefore, they have always been a major focus of neuroscience research. Two-photon laser scanning microscopy allows the visualization of synaptic structures in vivo, leading to many important findings. However, the identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias. Results We present an automated approach for the identification of synaptic structures in two-photon images. Axon boutons and dendritic spines are structurally distinct. They can be detected automatically using this image processing method. Then, synapses can be identified by integrating information from adjacent axon boutons and dendritic spines. In this study, we first detected the axonal boutons and dendritic spines respectively, and then identified synapses based on these results. Experimental results were validated manually, and the effectiveness of our proposed method was demonstrated. Conclusions This approach will helpful for neuroscientists to automatically analyze and quantify the formation, elimination and destabilization of the axonal boutons, dendritic spines and synapses. |
| first_indexed | 2024-12-18T15:22:28Z |
| format | Article |
| id | doaj.art-6f5ebf075a004776acc9f4996e5ed9c3 |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-12-18T15:22:28Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-6f5ebf075a004776acc9f4996e5ed9c32022-12-21T21:03:21ZengBMCBioData Mining1756-03812017-12-0110112310.1186/s13040-017-0161-5An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon imagesQiwei Xie0Xi Chen1Hao Deng2Danqian Liu3Yingyu Sun4Xiaojuan Zhou5Yang Yang6Hua Han7Research Base of Beijing Modern Manufacturing DevelopmentInstitute of Automation, Chinese Academy of SciencesFaculty of Information Technology, Macau University of Science and TechnologyInstitute of Neuroscience, Chinese Academy of SciencesBeijing Normal UniversityBeijing Normal UniversityInstitute of Neuroscience, Chinese Academy of SciencesInstitute of Automation, Chinese Academy of SciencesAbstract Background In the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefore, they have always been a major focus of neuroscience research. Two-photon laser scanning microscopy allows the visualization of synaptic structures in vivo, leading to many important findings. However, the identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias. Results We present an automated approach for the identification of synaptic structures in two-photon images. Axon boutons and dendritic spines are structurally distinct. They can be detected automatically using this image processing method. Then, synapses can be identified by integrating information from adjacent axon boutons and dendritic spines. In this study, we first detected the axonal boutons and dendritic spines respectively, and then identified synapses based on these results. Experimental results were validated manually, and the effectiveness of our proposed method was demonstrated. Conclusions This approach will helpful for neuroscientists to automatically analyze and quantify the formation, elimination and destabilization of the axonal boutons, dendritic spines and synapses.http://link.springer.com/article/10.1186/s13040-017-0161-5in vivo two-photon imagingSynapseBoutonSpineImage enhancement |
| spellingShingle | Qiwei Xie Xi Chen Hao Deng Danqian Liu Yingyu Sun Xiaojuan Zhou Yang Yang Hua Han An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images BioData Mining in vivo two-photon imaging Synapse Bouton Spine Image enhancement |
| title | An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images |
| title_full | An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images |
| title_fullStr | An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images |
| title_full_unstemmed | An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images |
| title_short | An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images |
| title_sort | automated pipeline for bouton spine and synapse detection of in vivo two photon images |
| topic | in vivo two-photon imaging Synapse Bouton Spine Image enhancement |
| url | http://link.springer.com/article/10.1186/s13040-017-0161-5 |
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