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DroplIT , an improved image analysis method for droplet identification in high‐throughput crystallization trials
Author(s) -
Vallotton Pascal,
Sun Changming,
Lovell David,
Fazio Vincent J.,
Newman Janet
Publication year - 2010
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s0021889810040963
Subject(s) - crystallization , throughput , limiting , identification (biology) , computer science , robotics , image processing , artificial intelligence , image (mathematics) , process engineering , robot , mechanical engineering , engineering , chemical engineering , biology , botany , wireless , telecommunications
The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate‐limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high‐throughput system to scrutinize massive crystallization campaigns automatically.