Toward automatic phenotyping of developing embryos from videos. Export

@article{Ning2005, 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 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.}, author = {Ning, Feng and Delhomme, Damien and Lecun, Yann and Piano, Fabio and Bottou, L\'{e}on and Barbano, Paolo E.}, citeulike-article-id = {2318683}, journal = {IEEE Trans Image Process}, keywords = {algorithms, and, animals, artificial, automated, caenorhabditis, computer-assisted, development, elegans, embryo, enhancement, fetal, from\_jabref, image, intelligence, interpretation, microscopy, nonmammalian, of, pattern, phase-contrast, phenotype, recognition, reproducibility, results, sensitivity, specificity, video}, month = {Sep}, number = {9}, pages = {1360--1371}, posted-at = {2008-02-01 09:55:09}, priority = {2}, title = {Toward automatic phenotyping of developing embryos from videos.}, volume = {14}, year = {2005} }

TY - JOUR ID - Ning2005 L3 - citeulike-article-id:2318683 N2 - 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. IS - 9 JF - IEEE Trans Image Process EP - 1371 TI - Toward automatic phenotyping of developing embryos from videos. VL - 14 SP - 1360 KW - algorithms KW - and KW - animals KW - artificial KW - automated KW - caenorhabditis KW - computer-assisted KW - development KW - elegans KW - embryo KW - enhancement KW - fetal KW - from_jabref KW - image KW - intelligence KW - interpretation KW - microscopy KW - nonmammalian KW - of KW - pattern KW - phase-contrast KW - phenotype KW - recognition KW - reproducibility KW - results KW - sensitivity KW - specificity KW - video AU - Ning, Feng AU - Delhomme, Damien AU - Lecun, Yann AU - Piano, Fabio AU - Bottou, Léon AU - Barbano, Paolo PY - 2005/Sep// ER -