title: Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates.
abstract: 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.
authors.affiliation: Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.
authors.forename: Christian A
authors.initials: CA
authors.lastname: Cumbaa
authors.forename: Angela
authors.initials: A
authors.lastname: Lauricella
authors.forename: Nancy
authors.initials: N
authors.lastname: Fehrman
authors.forename: Christina
authors.initials: C
authors.lastname: Veatch
authors.forename: Robert
authors.initials: R
authors.lastname: Collins
authors.forename: Joe
authors.initials: J
authors.lastname: Luft
authors.forename: George
authors.initials: G
authors.lastname: DeTitta
authors.forename: Igor
authors.initials: I
authors.lastname: Jurisica
mesh_terms: D019747:Aldose-Ketose Isomerases / Q000737:chemistry; D001185:Artificial Intelligence; D003460:Crystallization / Q000295:instrumentation* / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000145:classification*; D008832:Microchemistry / Q000295:instrumentation / Q000379:methods*; D036103:Nanotechnology; D015203:Reproducibility of Results; D012371:Robotics / Q000379:methods*
chemical_list: D019747:Aldose-Ketose Isomerases; C023298:xylose isomerase