A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues

• Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and eosin (H&E)-stained tissue sections. While various segmentation methods have been proposed, machine learning-based algorithms and in particular deep learning-based models have been shown to deliver better segmentation performance. In this work, we propose a novel approach to segment touching nuclei in H&E-stained microscopic images using U-Net-based models in two sequential stages. In the first stage, we perform semantic segmentation using a classification U-Net that separates nuclei from the background. In the second stage, the distance map of each nucleus is created using a regression U-Net. The final instance segmentation masks are then created using a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Deep learning

Digital pathology

Nuclei segmentation

Tissue analysis

U-Net

Machine learning

Pathology

Semantics

Tissue

Digital pathologies

Learning Based Models

Segmentation methods

Segmentation performance

Semantic segmentation

State-of-the-art algorithms

Image segmentation

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)