title: Automated training data generation for microscopy focus classification.
abstract: Image focus quality is of utmost importance in digital microscopes because the pathologist cannot accurately characterize the tissue state without focused images. We propose to train a classifier to measure the focus quality of microscopy scans based on an extensive set of image features. However, classifiers rely heavily on the quality and quantity of the training data, and collecting annotated data is tedious and expensive. We therefore propose a new method to automatically generate large amounts of training data using image stacks. Our experiments demonstrate that a classifier trained with the image stacks performs comparably with one trained with manually annotated data. The classifier is able to accurately detect out-of-focus regions, provide focus quality feedback to the user, and identify potential problems of the microscopy design.
authors.affiliation: GE Global Research, One Research Circle, Niskayuna, NY, 12309, USA. gaoda@ge.com
authors.forename: Dashan
authors.initials: D
authors.lastname: Gao
authors.forename: Dirk
authors.initials: D
authors.lastname: Padfield
authors.forename: Jens
authors.initials: J
authors.lastname: Rittscher
authors.forename: Richard
authors.initials: R
authors.lastname: McKay
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity