Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means

TL;DR

This paper introduces an unsupervised pathology image segmentation method combining representation learning with spherical K-means and conventional K-means, effectively segmenting lung cancer images without labeled data.

Contribution

It proposes a novel two-phase unsupervised segmentation approach using spherical K-means for feature learning and K-means for clustering, outperforming traditional methods.

Findings

Outperforms traditional K-means and Otsu methods in segmentation quality.

Achieves higher normalized mutual information score of 0.626.

Centroids are applicable to segment other slices from the same sample.

Abstract

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.