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J. Imaging 2017, 3(4), 61; https://doi.org/10.3390/jimaging3040061

Epithelium and Stroma Identification in Histopathological Images Using Unsupervised and Semi-Supervised Superpixel-Based Segmentation

1
School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Birmingham B5 7EG, UK
2
Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK
3
Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
This paper is an extended version of our paper published in Fouad, S.; Randell, D.; Galton, A.; Mehanna, H.; Landini, G. Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering. In Medical Image Understanding and Analysis; Valdés Hernández, M., González-Castro, V., Eds.; Springer: Edinburgh, UK, 2017; Volume 723, pp. 767–779.
*
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Revised: 5 December 2017 / Accepted: 6 December 2017 / Published: 11 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

We present superpixel-based segmentation frameworks for unsupervised and semi-supervised epithelium-stroma identification in histopathological images or oropharyngeal tissue micro arrays. A superpixel segmentation algorithm is initially used to split-up the image into binary regions (superpixels) and their colour features are extracted and fed into several base clustering algorithms with various parameter initializations. Two Consensus Clustering (CC) formulations are then used: the Evidence Accumulation Clustering (EAC) and the voting-based consensus function. These combine the base clustering outcomes to obtain a more robust detection of tissue compartments than the base clustering methods on their own. For the voting-based function, a technique is introduced to generate consistent labellings across the base clustering results. The obtained CC result is then utilized to build a self-training Semi-Supervised Classification (SSC) model. Unlike supervised segmentations, which rely on large number of labelled training images, our SSC approach performs a quality segmentation while relying on few labelled samples. Experiments conducted on forty-five hand-annotated images of oropharyngeal cancer tissue microarrays show that (a) the CC algorithm generates more accurate and stable results than individual clustering algorithms; (b) the clustering performance of the voting-based function outperforms the existing EAC; and (c) the proposed SSC algorithm outperforms the supervised methods, which is trained with only a few labelled instances. View Full-Text
Keywords: superpixel segmentation; consensus clustering; histopathology; image analysis; semi-supervised classification; self-training superpixel segmentation; consensus clustering; histopathology; image analysis; semi-supervised classification; self-training
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Fouad, S.; Randell, D.; Galton, A.; Mehanna, H.; Landini, G. Epithelium and Stroma Identification in Histopathological Images Using Unsupervised and Semi-Supervised Superpixel-Based Segmentation. J. Imaging 2017, 3, 61.

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