DeepAI AI Chat
Log In Sign Up

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

by   Takayasu Moriya, et al.

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.


page 3

page 5

page 6


Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

This paper presents a novel unsupervised segmentation method for 3D medi...

InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization

We propose a novel method for unsupervised semantic image segmentation b...

Representation Learning of Histopathology Images using Graph Neural Networks

Representation learning for Whole Slide Images (WSIs) is pivotal in deve...

Autoregressive Unsupervised Image Segmentation

In this work, we propose a new unsupervised image segmentation approach ...

Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation

Deep learning has not been routinely employed for semantic segmentation ...

Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines

This paper presents novel approaches for efficient feature extraction us...

Unsupervised Text Extraction from G-Maps

This paper represents an text extraction method from Google maps, GIS ma...