Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images
Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniqu...
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| Veröffentlicht in: | Neuroinformatics (Totowa, N.J.) N.J.), 2013, Vol.11 (1), p.5-29 |
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| creator | Jurrus, Elizabeth Watanabe, Shigeki Giuly, Richard J. Paiva, Antonio R. C. Ellisman, Mark H. Jorgensen, Erik M. Tasdizen, Tolga |
| description | Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes. |
| doi_str_mv | 10.1007/s12021-012-9149-y |
| format | Article |
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It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.</description><identifier>ISSN: 1539-2791</identifier><identifier>EISSN: 1559-0089</identifier><identifier>DOI: 10.1007/s12021-012-9149-y</identifier><identifier>PMID: 22644867</identifier><language>eng</language><publisher>New York: Springer-Verlag</publisher><subject>Applied sciences ; Artificial Intelligence ; Bioinformatics ; Biomedical and Life Sciences ; Biomedicine ; Boundaries ; Computational Biology/Bioinformatics ; Computer Appl. in Life Sciences ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Connectionism. 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This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.</description><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Bioinformatics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Boundaries</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computer Appl. in Life Sciences</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Connectionism. Neural networks</subject><subject>Connectome - methods</subject><subject>Connectomics</subject><subject>Data processing</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Electron Microscopy</subject><subject>Exact sciences and technology</subject><subject>Feature Detection</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image Processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Information, signal and communications theory</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Microscopy, Electron, Transmission - methods</subject><subject>Nervous system</subject><subject>Neural Networks, Computer</subject><subject>Neurology</subject><subject>Neurons</subject><subject>Neurons - ultrastructure</subject><subject>Neurosciences</subject><subject>Original Article</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. 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| subjects | Applied sciences Artificial Intelligence Bioinformatics Biomedical and Life Sciences Biomedicine Boundaries Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer science control theory systems Computer systems and distributed systems. User interface Connectionism. Neural networks Connectome - methods Connectomics Data processing Detection, estimation, filtering, equalization, prediction Electron Microscopy Exact sciences and technology Feature Detection Humans Image Interpretation, Computer-Assisted - methods Image Processing Image Processing, Computer-Assisted - methods Information, signal and communications theory Learning algorithms Machine Learning Microscopy, Electron, Transmission - methods Nervous system Neural Networks, Computer Neurology Neurons Neurons - ultrastructure Neurosciences Original Article Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Segmentation Signal and communications theory Signal, noise Software Telecommunications and information theory User-Computer Interface |
| title | Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images |
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