Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2014  |  Volume : 5  |  Issue : 1  |  Page : 19

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

1 Massachusetts Institute of Technology Lincoln Laboratory, Boston, MA, USA
2 Department of Biomedical Engineering, Center for Bioimage Informatics, Pittsburgh, USA
3 Department of Mathematics and Statistics, Air Force Institute of Technology, Wright Patterson Air Force Base, OH, USA
4 Department of Obstetrics and Gynecology, Magee-Womens Research Institute and Foundation of the University of Pittsburgh, Pittsburgh, USA
5 Department of Pathology, Children's Hospital of Pittsburgh of the University of Pittsburgh, Pittsburgh, PA, USA
6 Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh; Massachusetts Institute of Technology Lincoln Laboratory, Boston, MA, USA

Correspondence Address:
Michael T McCann
Department of Biomedical Engineering, Center for Bioimage Informatics, Pittsburgh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.135606

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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.

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