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.
This research has received funding from the Marie Sklodowska-Curie Actions of the European Union’s Horizon 2020 programme under REA grant agreement no. 675228.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arvidsson, I., Overgaard, N.C., Marginean, F.E., Krzyzanowska, A., Bjartell, A., Åström, K., Heyden, A.: Generalization of prostate cancer classification for multiple sites using deep learning. In: 15th IEEE International Symposium on Biomedical Imaging, pp. 191–194 (2018)
Cui, Y., Zhang, G., Liu, Z., Xiong, Z., Hu, J.: A deep learning algorithm for one-step contour aware nuclei segmentation of histopathological images. arXiv preprint arXiv:1803.02786 (2018)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review - current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations (2015)
Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging, pp. 1107–1110 (2009)
Naylor, P., Laé, M., Reyal, F., Walter, T.: Nuclei segmentation in histopathology images using deep neural networks. In: 14th IEEE International Symposium on Biomedical Imaging, pp. 933–936 (2017)
Naylor, P., Laé, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38, 448–459 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Yang, X., Li, H., Zhou, X.: Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy. IEEE Trans. Circuits Syst. I: Regul. Pap. 53(11), 2405–2414 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Smedby, Ö., Wang, C. (2019). A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-23937-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23936-7
Online ISBN: 978-3-030-23937-4
eBook Packages: Computer ScienceComputer Science (R0)