Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms
| Autor: | Umasankar Kandaswamy, Ziv Rotman, Ian Schillebeeckx, Vitaly A. Klyachko, Valeria Cavalli, Dana Watt |
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| Rok vydání: | 2013 |
| Předmět: |
Quality Control
Fluorescence-lifetime imaging microscopy Neurite Computer science Entropy Normal Distribution ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Fluorescent Antibody Technique Signal-To-Noise Ratio Machine learning computer.software_genre Hippocampus Fuzzy logic Article Synapse Automation Fuzzy Logic Artificial Intelligence Neurites Animals Cluster Analysis Image acquisition Segmentation Computer vision Cells Cultured Models Statistical business.industry General Neuroscience Biomedical image Dendrites Axons Rats ROC Curve Synapses Artificial intelligence business Algorithm computer Algorithms |
| Zdroj: | Journal of Neuroscience Methods. 213:84-98 |
| ISSN: | 0165-0270 |
| Popis: | High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. |
| Databáze: | OpenAIRE |
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