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.
Highlights
Automatic segmentation of digitised histological images into regions representing different anatomical or diagnostic types is of fundamental importance for developing digital pathology diagnostic tools
The results show that Evidence Accumulation Clustering (EAC)-Consensus Clustering (CC) and Vote-CC following jSLIC segmentation produce the most accurate results out of the individual clusterings tested (81% and 82%, respectively)
The results reveal that CC methods result in greater consistency in performance over individual clustering methods as illustrated by lower standard deviations of the Rand Index (RI) and F1-scores
Summary
Automatic segmentation of digitised histological images into regions representing different anatomical or diagnostic types is of fundamental importance for developing digital pathology diagnostic tools. Superpixel segmentation is an advanced method to group image pixels with similar colour properties into atomic regions to simplify the data in the pixel grid [1]. Superpixel methods have been combined with pattern recognition techniques for image segmentation (e.g., [2]). Where certain features (e.g., colour, morphology) are fed to pattern recognition procedures that assign each superpixel to expected histological classes. Supervised analysis methods are typically built from labelled training sets to predict the classes of novel unlabelled data and they require access to ‘ground. Unsupervised approaches (clustering analysis) do not require pre-labelled training sets for their learning but instead rely on certain similarity measures to group data into separate homogeneous clusters. In histopathological imaging analysis, clustering is of particular interest because of its potential as an exploratory tool that might provide information about hidden anatomical or functional structures in images
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