Nuclei Segmentation in Histopathology Images using Deep Learning with Local and Global Views

TL;DR

This paper introduces a deep learning approach for nuclei segmentation in histopathology images that leverages both local and global views to improve accuracy, addressing patch border issues and outperforming existing models.

Contribution

It proposes a novel deep learning method combining local and global patches for more accurate nuclei segmentation in histopathology images.

Findings

Outperforms baseline segmentation models.

Addresses patch border mispredictions.

Effective on multi-organ histopathology dataset.

Abstract

Digital pathology is one of the most significant developments in modern medicine. Pathological examinations are the gold standard of medical protocols and play a fundamental role in diagnosis. Recently, with the advent of digital scanners, tissue histopathology slides can now be digitized and stored as digital images. As a result, digitized histopathological tissues can be used in computer-aided image analysis programs and machine learning techniques. Detection and segmentation of nuclei are some of the essential steps in the diagnosis of cancers. Recently, deep learning has been used for nuclei segmentation. However, one of the problems in deep learning methods for nuclei segmentation is the lack of information from out of the patches. This paper proposes a deep learning-based approach for nuclei segmentation, which addresses the problem of misprediction in patch border areas. We use both local and global patches to predict the final segmentation map. Experimental results on the Multi-organ histopathology dataset demonstrate that our method outperforms the baseline nuclei segmentation and popular segmentation models.