|Acta Crystallographica Section D: Biological Crystallography (2003) 59(Pt 9):1619-27|
|Northeast Structural Genomics Consortium|
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A technique for automatically evaluating microbatch (400 nl) protein-crystallization trials is described. ...
This method addresses analysis problems introduced at the sub-microlitre scale, including non-uniform lighting and irregular droplet boundaries. The droplet is segmented from the well using a loopy probabilistic graphical model with a two-layered grid topology. A vector of 23 features is extracted from the droplet image using the Radon transform for straight-edge features and a bank of correlation filters for microcrystalline features. Image classification is achieved by linear discriminant analysis of its feature vector. The results of the automatic method are compared with those of a human expert on 32 1536-well plates. Using the human-labeled images as ground truth, this method classifies images with 85% accuracy and a ROC score of 0.84. This result compares well with the experimental repeatability rate, assessed at 87%. Images falsely classified as crystal-positive variously contain speckled precipitate resembling microcrystals, skin effects or genuine crystals falsely labeled by the human expert. Many images falsely classified as crystal-negative variously contain very fine crystal features or dendrites lacking straight edges. Characterization of these misclassifications suggests directions for improving the method.
|methods classification instrumentation chemistry |
|Image Processing, Computer-Assisted Microchemistry Nanotechnology Crystallization Robotics Reproducibility of Results Artificial Intelligence Aldose-Ketose Isomerases |
|49 (Last update: 04/21/2018 4:18:57pm)|
Automated crystallography image classification
|Acta Crystallogr D Biol Crystallogr. 2003 Sep;59(Pt 9):1619-27. Epub 2003 Aug 19.|