  This paper presents a novel method for unsupervised segmentation of pathology
images. Staging of lung cancer is a major factor of prognosis. Measuring the
maximum dimensions of the invasive component in a pathology images is an
essential task. Therefore, image segmentation methods for visualizing the
extent of invasive and noninvasive components on pathology images could support
pathological examination. However, it is challenging for most of the recent
segmentation methods that rely on supervised learning to cope with unlabeled
pathology images. In this paper, we propose a unified approach to unsupervised
representation learning and clustering for pathology image segmentation. Our
method consists of two phases. In the first phase, we learn feature
representations of training patches from a target image using the spherical
k-means. The purpose of this phase is to obtain cluster centroids which could
be used as filters for feature extraction. In the second phase, we apply
conventional k-means to the representations extracted by the centroids and then
project cluster labels to the target images. We evaluated our methods on
pathology images of lung cancer specimen. Our experiments showed that the
proposed method outperforms traditional k-means segmentation and the
multithreshold Otsu method both quantitatively and qualitatively with an
improved normalized mutual information (NMI) score of 0.626 compared to 0.168
and 0.167, respectively. Furthermore, we found that the centroids can be
applied to the segmentation of other slices from the same sample.
