Date of Original Version
© 2014 Bhagavatula R. This is an open‑access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract or Description
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.
This work is licensed under a Creative Commons Attribution 4.0 License.
Journal of Pathology Informatics, 5.