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

WHITE AMELIA; LEES B; KAO HL; CIPRIANI PG; MUNARRIZ E; PAABY BA; ERICKSON K; GUZMAN S; RATTANAKORN K; SONTAG E; GEIGER D; GUNSALUS KC; PIANO F

IEEE TRANSACTION ON MEDICAL IMAGING

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2013 vol. 32 p. 1791 - 1803

0278-0062

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.