Toward automatic phenotyping of developing embryos from videos.
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.
Ning
Feng
F
Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA. fengning@cs.nyu.edu
Delhomme
Damien
D
LeCun
Yann
Y
Piano
Fabio
F
Bottou
Léon
L
Barbano
Paolo Emilio
PE
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D017173:Caenorhabditis elegans / Q000033:anatomy & histology* / Q000145:classification / Q000196:embryology* / Q000254:growth & development; D004625:Embryo, Nonmammalian / Q000166:cytology; D047109:Fetal Development / Q000502:physiology; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity