SwePub - sökning: L773:9783030239367

Numrering	Referens	Omslagsbild	Hitta
1.	Mahbod, A., et al. (författare) A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues 2019 Ingår i: Digital Pathology. - Cham : Springer Verlag. - 9783030239367 ; , s. 75-82 Konferensbidrag (refereegranskat)abstract 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.

Mahbod, A., et al. (författare)
A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues
2019
Ingår i: Digital Pathology. - Cham : Springer Verlag. - 9783030239367 ; , s. 75-82
Konferensbidrag (refereegranskat)abstract
- 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.

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