Abstract
Nucleus segmentation is a fundamental task in digital pathology analysis. However, it is labor-expensive and time-consuming to manually annotate the pixel-level full nucleus masks, while it is easier to make point annotations. In this paper, we propose a coarse-to-fine weakly-supervised framework to train the segmentation model from only point annotations to reduce the labor cost of generating pixel-level masks. Our coarse-to-fine strategy can improve segmentation performance progressively in a self-stimulated learning manner. Specifically, to generate coarse segmentation masks, we employ a self-supervision strategy using clustering to perform the binary classification. To avoid trivial solutions, our model is sparsely supervised by annotated positive points and geometric-constrained negative boundaries, via point-to-region spatial expansion and Voronoi partition, respectively. Then, to generate fine segmentation masks, the prior knowledge of edges in the unadorned image is additionally utilized by our proposed contour-sensitive constraint to further tune the nucleus contours. Experimental results on two public datasets show that our model trained with weakly-supervised data (i.e., point annotations) achieves competitive performance compared with the model trained with fully supervised data (i.e., full nucleus masks). The code is made publicly available at https://github.com/tiankuan93/C2FNet.
K. Tian and J. Zhang—These authors contribute equally to this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: counting by localization with point supervision. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 560–576. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_34
Nishimura, K., Ker, D.F.E., Bise, R.: Weakly supervised cell instance segmentation by propagating from detection response. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 649–657. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_72
Qu, H., Wu, P., Huang, Q., et al.: Weakly supervised deep nuclei segmentation using points annotation in histopathology images. In: International Conference on Medical Imaging with Deep Learning, pp. 390–400 (2019)
Chamanzar, A., Nie, Y.: Weakly supervised multi-task learning for cell detection and segmentation. arXiv preprint arXiv:1910.12326 (2019)
Yoo, I., Yoo, D., Paeng, K.: PseudoEdgeNet: nuclei segmentation only with point annotations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 731–739. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_81
Hershey, J.R., Chen, Z., Le Roux, J., et al.: Deep clustering: discriminative embeddings for segmentation and separation. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 31–35 (2016)
Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, pp. 1–4 (2017)
Kumar, N., Verma, R., Sharma, S., et al.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)
Naylor, P., Lae, M., Reyal, F., et al.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448–459 (2018)
Sirinukunwattana, K., Snead, D.R.J., Rajpoot, N.M.: A stochastic polygons model for glandular structures in colon histology images. IEEE Trans. Med. Imaging 34(11), 2366–2378 (2015)
Sadanandan, S.K., Ranefall, P., Le Guyader, S., et al.: Automated training of deep convolutional neural networks for cell segmentation. Sci. Rep. 7(1), 1–7 (2017)
Hatipoglu, N., Bilgin, G.: Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med. Biol. Eng. Comput. 55(10), 1829–1848 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tian, K. et al. (2020). Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-59722-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59721-4
Online ISBN: 978-3-030-59722-1
eBook Packages: Computer ScienceComputer Science (R0)