Unsupervised morphological segmentation of tissue compartments in histopathological images

Shereen Fouad; David Randell; Antony Galton; Hisham Mehanna; Gabriel Landini

doi:10.1371/journal.pone.0188717

Unsupervised morphological segmentation of tissue compartments in histopathological images

Shereen Fouad^*, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

30 Citations (SciVal)

Original language	English
Article number	e0188717
Journal	PLoS ONE
Volume	12
Issue number	11
DOIs	https://doi.org/10.1371/journal.pone.0188717
Publication status	Published (VoR) - Nov 2017

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy” (https://www.epsrc.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”.

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AU - Mehanna, Hisham

AU - Landini, Gabriel

N1 - Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy” (https://www.epsrc.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”. Publisher Copyright: © 2017 Fouad et al. 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.

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Unsupervised morphological segmentation of tissue compartments in histopathological images

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