Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation

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

doi:10.3390/jimaging3040061

Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation

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

^*Corresponding author for this work

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

9 Citations (SciVal)

Original language	English
Article number	61
Journal	Journal of Imaging
Volume	3
Issue number	4
DOIs	https://doi.org/10.3390/jimaging3040061
Publication status	Published (VoR) - Dec 2017

Funding

Acknowledgments: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 "Novel context-based segmentation algorithms for intelligent microscopy".

Keywords

Consensus clustering
Histopathology
Image analysis
Self-training
Semi-supervised classification
Superpixel segmentation

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10.3390/jimaging3040061

Cite this

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title = "Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation",

keywords = "Consensus clustering, Histopathology, Image analysis, Self-training, Semi-supervised classification, Superpixel segmentation",

author = "Shereen Fouad and David Randell and Antony Galton and Hisham Mehanna and Gabriel Landini",

note = "Funding Information: Acknowledgments: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”. Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 {"}Novel context-based segmentation algorithms for intelligent microscopy{"}. Publisher Copyright: {\textcopyright} 2017 by the authors.",

year = "2017",

month = dec,

doi = "10.3390/jimaging3040061",

language = "English",

volume = "3",

journal = "Journal of Imaging",

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T1 - Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation

AU - Fouad, Shereen

AU - Randell, David

AU - Galton, Antony

AU - Mehanna, Hisham

AU - Landini, Gabriel

N1 - Funding Information: Acknowledgments: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”. Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through funding under grant EP/M023869/1 "Novel context-based segmentation algorithms for intelligent microscopy". Publisher Copyright: © 2017 by the authors.

PY - 2017/12

Y1 - 2017/12

KW - Consensus clustering

KW - Histopathology

KW - Image analysis

KW - Self-training

KW - Semi-supervised classification

KW - Superpixel segmentation

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DO - 10.3390/jimaging3040061

M3 - Article

AN - SCOPUS:85047113275

SN - 2313-433X

VL - 3

JO - Journal of Imaging

JF - Journal of Imaging

IS - 4

M1 - 61

ER -

Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation

Funding

Keywords

Access to Document

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