DevStaR: High-throughput quantification of C. elegans developmental stages

Amelia G. White, Brandon Lees, Huey Ling Kao, P. Giselle Cipriani, Eliana Munarriz, Annalise B. Paaby, Katherine Erickson, Sherly Guzman, Kirk Rattanakorn, Eduardo Sontag, Davi Geiger, Kristin C. Gunsalus, Fabio Piano

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6521338
Pages (from-to)1791-1803
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number10
DOIs
StatePublished - Oct 14 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • C. elegans
  • Computer vision
  • High-throughput phenotyping
  • Object recognition

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