Abstract
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant 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
Carpenter, A.E., et al.: Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), R100 (2006)
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., Heng, P.A.: DCAN: deep contour-aware networks for object instance segmentation from histology images. Med. Image Anal. 36, 135–146 (2017)
Cheng, J., Rajapakse, J.C., et al.: Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans. Biomed. Eng. 56(3), 741–748 (2009)
Dou, Q., et al.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer series in statistics, vol. 1. Springer, New York (2001). https://doi.org/10.1007/978-0-387-21606-5
Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: ICLR 2017 (2017)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)
Irshad, H., et al.: Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. In: Pacific Symposium on Biocomputing, pp. 294–305. World Scientific (2014)
Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: ICML (2018)
Jung, C., Kim, C.: Segmenting clustered nuclei using H-minima transform-based marker extraction andcontour parameterization. IEEE Trans. Biomed. Eng. 57(10), 2600–2604 (2010)
Kazeminia, S., et al.: GANs for medical image analysis. arXiv preprint arXiv:1809.06222 (2018)
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)
Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: IEEE CVPR (2017)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: IEEE CVPR (2018)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.05101 (2017)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: IEEE ISBI (2009)
Oda, H., et al.: BESNet: boundary-enhanced segmentation of cells in histopathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 228–236. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_26
Pantanowitz, L.: Digital images and the future of digital pathology. J. Pathol. Inform. 1, 15 (2010)
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: A loss correction approach. In: IEEE CVPR (2017)
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596 (2014)
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
Schindelin, J., et al.: Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676 (2012)
Veta, M., et al.: Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Mod. Pathol. 25(12), 1559 (2012)
Xue, C., Dou, Q., Shi, X., Chen, H., Heng, P.A.: Robust learning at noisy labeled medical images: applied to skin lesion classification (2019)
Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. arXiv preprint arXiv:1809.07294 (2018)
Acknowledgments
This work was supported by Hong Kong Innovation and Technology Fund (Project No. ITS/041/16), Guangdong province science and technology plan project (No. 2016A020220013).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, Y., Onder, O.F., Dou, Q., Tsougenis, E., Chen, H., Heng, PA. (2019). CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation. 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_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-20351-1_53
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)