Epithelium and Stroma Identification in Histopathological Images using Unsupervised and Semi-supervised Superpixel-based Segmentation

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

Multilevel Segmentation Of Histopathological Images Using Cooccurrence Of Tissue Objects

This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach-the object cooccurrence features together with the multilevel segmentation algorithm-is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches.Wo