Automated analysis of Physarum network structure and dynamics
We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-trut...
| Glavni autori: | , , , , , |
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| Format: | Journal article |
| Izdano: |
IOP Publishing
2017
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| _version_ | 1826258139056439296 |
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| author | Fricker, MD Akita, D Heaton, LLM Jones, N Obara, B Nakagaki, T |
| author_facet | Fricker, MD Akita, D Heaton, LLM Jones, N Obara, B Nakagaki, T |
| author_sort | Fricker, MD |
| collection | OXFORD |
| description | We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray's law. This work was presented at PhysNet 2015. |
| first_indexed | 2024-03-06T18:29:18Z |
| format | Journal article |
| id | oxford-uuid:0917757a-7f0f-4f34-ade7-fe27aa614eb2 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T18:29:18Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:0917757a-7f0f-4f34-ade7-fe27aa614eb22022-03-26T09:16:19ZAutomated analysis of Physarum network structure and dynamicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0917757a-7f0f-4f34-ade7-fe27aa614eb2Symplectic Elements at OxfordIOP Publishing2017Fricker, MDAkita, DHeaton, LLMJones, NObara, BNakagaki, TWe evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray's law. This work was presented at PhysNet 2015. |
| spellingShingle | Fricker, MD Akita, D Heaton, LLM Jones, N Obara, B Nakagaki, T Automated analysis of Physarum network structure and dynamics |
| title | Automated analysis of Physarum network structure and dynamics |
| title_full | Automated analysis of Physarum network structure and dynamics |
| title_fullStr | Automated analysis of Physarum network structure and dynamics |
| title_full_unstemmed | Automated analysis of Physarum network structure and dynamics |
| title_short | Automated analysis of Physarum network structure and dynamics |
| title_sort | automated analysis of physarum network structure and dynamics |
| work_keys_str_mv | AT frickermd automatedanalysisofphysarumnetworkstructureanddynamics AT akitad automatedanalysisofphysarumnetworkstructureanddynamics AT heatonllm automatedanalysisofphysarumnetworkstructureanddynamics AT jonesn automatedanalysisofphysarumnetworkstructureanddynamics AT obarab automatedanalysisofphysarumnetworkstructureanddynamics AT nakagakit automatedanalysisofphysarumnetworkstructureanddynamics |