The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Visualization of image data from cells to organisms
Open accessAbstract
Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
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Cites
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“As the number of angular samples increases (for example, in Q-Ball imagin”
Dynamics of Gray Matter Loss in Alzheimer's Disease
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Game On, Science - How Video Game Technology May Help Biologists Tackle Visualization Challenges
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A bird's-eye view of deep learning in bioimage analysis
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Review: application of artificial intelligence in phenomics
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Metadata
- Authors
- Walter, T., Shattuck, D. W., Baldock, R., Bastin, M. E., Carpenter, A. E., Duce, S., Ellenberg, J., Fraser, A., Hamilton, N., Pieper, S., Ragan, M. A., Schneider, J. E., Tomancak, P., & Hériché, J.-K.
- Venue
- Nature Methods
- Published
- 2010-03
- Volume
- 7
- Issue
- S3
- Pages
- S26-S41
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1976443465
- Cited by
- 266
- References
- 11
- FWCI
- 19.84
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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