Abstract
Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still challenging. The classic U-net has not achieved satisfactory results on this task, because it mixes the information of different scales that affect each other, which limits the segmentation accuracy of the model. To solve this problem, we propose a progressive growing U-net (PGU-net+) model, which uses two paradigms to extract image features at different scales in a more independent way. First, we add residual modules between different scales of U-net, which enforces the model to learn the approximate shape of the annotation in the coarser scale, and to learn the residual between the annotation and the approximate shape in the finer scale. Second, we start to train the model with the coarsest part and then progressively add finer part to the training until the full model is included. When we train a finer part, we will reduce the learning rate of the previous coarser part, which further ensures that the model independently extracts information from different scales. We conduct several comparative experiments on the Herlev dataset. The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
J. Zhao and L. Dai—Joint First Authors.
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References
Bora, K., Chowdhury, M., Mahanta, L.B., Kundu, M.K., Das, A.K.: Automated classification of pap smear images to detect cervical dysplasia. Comput. Methods Programs Biomed. 138, 31–47 (2017)
Tareef, A., Song, Y., Cai, W., Feng, D.D., Chen, M.: Automated three-stage nucleus and cytoplasm segmentation of overlapping cells. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 865–870. IEEE (2014)
Hai-Shan, W., Barba, J., Gil, J.: A parametric fitting algorithm for segmentation of cell images. IEEE Trans. Biomed. Eng. 45(3), 400–407 (1998)
Plissiti, M.E., Nikou, C., Charchanti, A.: Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Trans. Inf Technol. Biomed. 15(2), 233–241 (2010)
Lassouaoui, N., Hamami, L.: Genetic algorithms and multifractal segmentation of cervical cell images. In: Proceedings of Seventh International Symposium on Signal Processing and its Applications, vol. 2, pp. 1–4. IEEE (2003)
Zhang, L., et al.: Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts. Comput. Med. Imaging Graph. 38(5), 369–380 (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
Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Trans. Biomed. Eng. 62(10), 2421–2433 (2015)
Song, Y., et al.: Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans. Med. Imaging 36(1), 288–300 (2016)
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480 (2017)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
GençTav, A., Aksoy, S., ÖNder, S.: Unsupervised segmentation and classification of cervical cell images. Pattern Recognit. 45(12), 4151–4168 (2012)
Chankong, T., Theera-Umpon, N., Auephanwiriyakul, S.: Automatic cervical cell segmentation and classification in pap smears. Comput. Methods Programs Biomed. 113(2), 539–556 (2014)
Gautam, S., Bhavsar, A., Sao, A.K., Harinarayan, K.K.: CNN based segmentation of nuclei in pap-smear images with selective pre-processing. In: Medical Imaging 2018: Digital Pathology, vol. 10581, p. 105810X. International Society for Optics and Photonics (2018)
Zhao, J., Li, Q., Li, X., Li, H., Zhang, L.: Automated segmentation of cervical nuclei in pap smear images using deformable multi-path ensemble model. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1514–1518. IEEE (2019)
Acknowledgments
This work is supported in part by the National Key Research and Development Program of China under Grant 2018YFC0910700 and the National Natural Science Foundation of China (NSFC) under Grants 81801778, 11831002, 11701018.
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Zhao, J. et al. (2020). PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation. In: Li, Q., Leahy, R., Dong, B., Li, X. (eds) Multiscale Multimodal Medical Imaging. MMMI 2019. Lecture Notes in Computer Science(), vol 11977. Springer, Cham. https://doi.org/10.1007/978-3-030-37969-8_7
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DOI: https://doi.org/10.1007/978-3-030-37969-8_7
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