DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imagin...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007012 |
| _version_ | 1818901938717065216 |
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| author | Victor Kulikov Syuan-Ming Guo Matthew Stone Allen Goodman Anne Carpenter Mark Bathe Victor Lempitsky |
| author_facet | Victor Kulikov Syuan-Ming Guo Matthew Stone Allen Goodman Anne Carpenter Mark Bathe Victor Lempitsky |
| author_sort | Victor Kulikov |
| collection | DOAJ |
| description | Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet. |
| first_indexed | 2024-12-19T20:27:42Z |
| format | Article |
| id | doaj.art-5c994a9db1024b0ba6e419b7bc0179a0 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-19T20:27:42Z |
| publishDate | 2019-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5c994a9db1024b0ba6e419b7bc0179a02022-12-21T20:06:48ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-05-01155e100701210.1371/journal.pcbi.1007012DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.Victor KulikovSyuan-Ming GuoMatthew StoneAllen GoodmanAnne CarpenterMark BatheVictor LempitskyNeuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.https://doi.org/10.1371/journal.pcbi.1007012 |
| spellingShingle | Victor Kulikov Syuan-Ming Guo Matthew Stone Allen Goodman Anne Carpenter Mark Bathe Victor Lempitsky DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. PLoS Computational Biology |
| title | DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. |
| title_full | DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. |
| title_fullStr | DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. |
| title_full_unstemmed | DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. |
| title_short | DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. |
| title_sort | dognet a deep architecture for synapse detection in multiplexed fluorescence images |
| url | https://doi.org/10.1371/journal.pcbi.1007012 |
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