Abstract: 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 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.

@article{ning-delhomme-2005,
  author = {Ning, Feng and Delhomme, Damien and Yann {LeCun} and Piano, Fabio and Bottou, {L\'{e}on} and Barbano, Paolo Emilio},
  title = {Toward Automatic Phenotyping of Developing Embryos from Videos},
  journal = {IEEE Transactions in Image Processing},
  year = {2005},
  volume = {14},
  number = {9},
  pages = {1360-1371},
  month = {September},
  url = {http://leon.bottou.org/papers/ning-delhomme-2005},
}