Abstract
Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclear instances. The instance differentiation process are thus largely simplified and easier to understand. Experiments demonstrate the effectiveness of Center Vector Encoding, where our method outperforms state-of-the-arts by a clear margin.
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
References
Arbelle, A., Raviv, T.R.: Microscopy cell segmentation via adversarial neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018, pp. 645–648. IEEE (2018)
Bamford, P.: Automating cell segmentation evaluation with annotated examples. In: APRS Workshop on Digital Image Computing, pp. 21–25 (2003)
Belsare, A., Mushrif, M., Pangarkar, M.: Breast epithelial duct region segmentation using intuitionistic fuzzy based multi-texture image map. In: 2017 14th IEEE India Council International Conference (INDICON), pp. 1–6. IEEE (2017)
Chen, L.C., Hermans, A., Papandreou, G., Schroff, F., Wang, P., Adam, H.: MaskLab: instance segmentation by refining object detection with semantic and direction features. arXiv preprint arXiv:1712.04837 2 (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. CoRR abs/1803.02786. http://arxiv.org/abs/1803.02786 (2018)
Fu, D., Xie, X.S.: Reliable cell segmentation based on spectral phasor analysis of hyperspectral stimulated raman scattering imaging data. Anal. Chem. 86(9), 4115–4119 (2014)
Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)
Gurcan, M.N., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147 (2009)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)
Ho, D.J., Fu, C., Salama, P., Dunn, K.W., Delp, E.J.: Nuclei segmentation of fluorescence microscopy images using three dimensional convolutional neural networks (2017)
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)
Li, G., et al.: 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol. 8(1), 40 (2007). https://doi.org/10.1186/1471-2121-8-40
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. CoRR abs/1803.01534. http://arxiv.org/abs/1803.01534 (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597. http://arxiv.org/abs/1505.04597 (2015)
Sadanandan, S.K., Ranefall, P., Le Guyader, S., Wählby, C.: Automated training of deep convolutional neural networks for cell segmentation. Sci. Rep. 7(1), 7860 (2017)
Stegmaier, J., et al.: Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018, pp. 382–386. IEEE (2018)
Su, H., Yin, Z., Huh, S., Kanade, T.: Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features. Med. Image Anal. 17(7), 746–765 (2013)
Wang, P., Hu, X., Li, Y., Liu, Q., Zhu, X.: Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Sig. Process. 122, 1–13 (2016)
Wang, Z., Li, H.: Generalizing cell segmentation and quantification. BMC Bioinform. 18(1), 189 (2017)
Xing, F., Xie, Y., Yang, L.: An automatic learning-based framework for robust nucleus segmentation. IEEE Trans. Med. Imaging 35(2), 550–566 (2016)
Yin, Z., Bise, R., Chen, M., Kanade, T.: Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 125–128. IEEE (2010)
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. arXiv preprint arXiv:1707.06484 (2017)
Zhang, X., Liu, W., Dundar, M., Badve, S., Zhang, S.: Towards large-scale histopathological image analysis: Hashing-based image retrieval. IEEE Trans. Med. Imaging 34(2), 496–506 (2015)
Zhang, X., Xing, F., Su, H., Yang, L., Zhang, S.: High-throughput histopathological image analysis via robust cell segmentation and hashing. Med. Image Anal. 26(1), 306–315 (2015)
Zhou, Y., Kuijper, A., Heise, B., He, L.: Cell segmentation using level set method. NA (2007)
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
Li, J., Hu, Z., Yang, S. (2019). Accurate Nuclear Segmentation with Center Vector Encoding. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-20351-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20350-4
Online ISBN: 978-3-030-20351-1
eBook Packages: Computer ScienceComputer Science (R0)