DevStaR: High-Throughput Quantification of C. elegans Developmental Stages
| Autor: | Katherine Erickson, Fabio Piano, Kristin C. Gunsalus, Amelia G. White, P. Giselle Cipriani, Eliana Munarriz, Eduardo D. Sontag, Davi Geiger, Kirk Rattanakorn, Sherly Guzman, Annalise B. Paaby, Huey-Ling Kao, Brandon Lees |
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| Rok vydání: | 2013 |
| Předmět: |
C. ELEGANS
Computer science DEVELOPMENTAL STAGE Genomics Machine learning computer.software_genre OBJECT RECOGNITION Genetic model Image Processing Computer-Assisted Animals Electrical and Electronic Engineering Caenorhabditis elegans Throughput (business) Organism Life Cycle Stages Microscopy Developmental stage Radiological and Ultrasound Technology business.industry HIGH-THROUGHPUT PHENOTYPING Animal development Embryonic stem cell Computer Science Applications Phenotype Ciencias de la Computación e Información Artificial intelligence business Ciencias de la Información y Bioinformática computer CIENCIAS NATURALES Y EXACTAS Algorithms Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 32:1791-1803 |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2013.2265092 |
| Popis: | We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens. Fil: White, Amelia G.. Rutgers University. Department of Computational Biology and Molecular Biophysics; Estados Unidos de América; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Lees, Brandon. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Kao, Huey-Ling. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Cipriani, P. Giselle. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Munarriz, Eliana Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina; New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Paaby, Annalyse B.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Erickson, Katherine. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Guzman, Sherly. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Rattanakorn, Kirk. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Sontag, Eduardo. Rutgers University. Department of Mathematics; Estados Unidos de América; Fil: Geiger, Davi. New York University. Courant Institute of Mathematical Sciences. Department of Computer Science; Estados Unidos de América; Fil: Gunsalus, Kristin C.. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América; Fil: Piano, Fabio. New York University. Department of Biology. Center for Genomics and Systems Biology; Estados Unidos de América |
| Databáze: | OpenAIRE |
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