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

• Main Authors: Shereen Fouad, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini
• Format: Article
• Language: English
• Published: MDPI AG 2017-12-01
• Series: Journal of Imaging
• Subjects: superpixel segmentation; consensus clustering; histopathology; image analysis; semi-supervised classification; self-training
• Online Access: https://www.mdpi.com/2313-433X/3/4/61

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.