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Toward automatic phenotyping of developing embryos from videos
Open accessAbstract
We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.
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Cited by
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Self-citationFully convolutional networks for semantic segmentation
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A fast learning algorithm for deep belief nets
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Fully convolutional networks for semantic segmentation
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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
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Metadata
- Authors
- Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., & Barbano, P. E.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2005-09
- Volume
- 14
- Issue
- 9
- Pages
- 1360-1371
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2158778629
- Cited by
- 261
- References
- 4
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- green
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