title: A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples.
abstract: We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples.
  
  
  BACKGROUND
  H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness.
  
  
  METHODS
  We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density". These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm.
  
  
  RESULTS
  Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach.
  
  
  CONCLUSIONS
  The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.
authors.affiliation: Massachusetts Institute of Technology Lincoln Laboratory, Boston, MA, USA.
authors.forename: Ramamurthy
authors.initials: R
authors.lastname: Bhagavatula
authors.affiliation: Department of Biomedical Engineering, Center for Bioimage Informatics, Pittsburgh, USA.
authors.forename: Michael T
authors.initials: MT
authors.lastname: McCann
authors.affiliation: Department of Mathematics and Statistics, Air Force Institute of Technology, Wright-Patterson Air Force Base, OH, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Fickus
authors.affiliation: Department of Obstetrics and Gynecology, Magee-Womens Research Institute and Foundation of the University of Pittsburgh, Pittsburgh, USA.
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Castro
authors.affiliation: Department of Pathology, Children's Hospital of Pittsburgh of the University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Ozolek
authors.affiliation: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA ; Department of Biomedical Engineering, Center for Bioimage Informatics, Pittsburgh, USA.
authors.forename: Jelena
authors.initials: J
authors.lastname: Kovacevic
keywords: Automated histology; classification; segmentation