Ontological Levels in Histological Imaging
Frontiers in Artificial Intelligence and Applications
In this paper we present an ontological perspective on ongoing work in histological and histopathological imaging involving the quantitative and algorithmic analysis of digitised images of cells and tissues. We present the derivation of consistent histological models from initially captured images of prepared tissue samples as a progression through a number of ontological levels, each populated by its distinctive classes of entities related in systematic ways to entities at other levels. We see this work as contributing to ongoing efforts to provide a consistent and widely accepted suite of ontological resources such as those currently constituting the OBO Foundry, and where possible we draw links between our work and existing ontologies within that suite.
This research is supported by EPSRC through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”.
Paper presented at the 9th edition of the Formal Ontology in Information Systems conference, FOIS 2016, July 6–9, 2016, Annecy, France
This is the author accepted manuscript. The final version is available from IOS Press via the DOI in this record.
Vol. 283 (Formal Ontology in Information Systems), pp. 271 - 284