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A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues
Vise andre og tillknytning
2019 (engelsk)Inngår i: Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings / [ed] Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana, Springer Verlag , 2019, s. 75-82Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Springer Verlag , 2019. s. 75-82
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Emneord [en]
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
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-262448DOI: 10.1007/978-3-030-23937-4_9Scopus ID: 2-s2.0-85069146581ISBN: 9783030239367 (tryckt)OAI: oai:DiVA.org:kth-262448DiVA, id: diva2:1362772
Konferanse
15th European Congress on Digital Pathology, ECDP 2019, Warwick, United Kingdom 10-13 April 2019
Merknad

QC 20191021

Tilgjengelig fra: 2019-10-21 Laget: 2019-10-21 Sist oppdatert: 2019-10-21bibliografisk kontrollert

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