title: Removing defocused objects from single focal plane scans of cytological slides.
abstract: BACKGROUND
  Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine.
  
  
  METHODS
  In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry.
  
  
  CONCLUSIONS
  We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically.
authors.affiliation: Faculty of Electrical Engineering and Information Technology, Institute of Imaging and Computer Vision, RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: David
authors.initials: D
authors.lastname: Friedrich
authors.affiliation: Institute of Pathology, Hospital Düren, 52351 Düren, Germany.
authors.forename: Alfred
authors.initials: A
authors.lastname: Böcking
authors.affiliation: Faculty of Electrical Engineering and Information Technology, Institute of Imaging and Computer Vision, RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Dietrich
authors.initials: D
authors.lastname: Meyer-Ebrecht
authors.affiliation: Faculty of Electrical Engineering and Information Technology, Institute of Imaging and Computer Vision, RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Dorit
authors.initials: D
authors.lastname: Merhof
keywords: Autofocus; DNA image cytometry; cytology; machine learning; virtual microscopy