Abstract
Identification and analysis of leukocytes (white blood cells, WBC) in blood smear images play a vital role in the diagnosis of many diseases, including infections, leukemia, and acquired immune deficiency syndrome (AIDS). However, it remains difficult to accurately segment and identify leukocytes under variable imaging conditions, such as variable light conditions and staining degrees, the presence of dyeing impurities, and large variations in cell appearances, e.g., size, color, and shape of cells. In this paper, we propose an end-to-end leukocyte segmentation algorithm that uses pixel-level prior information for supervised training of a deep convolutional neural network. Specifically, a context-aware feature encoder is first introduced to extract multi-scale leukocyte features. Then, a feature refinement module based on the residual network is designed to extract more discriminative features. Finally, a finer segmentation mask of leukocytes is reconstructed by a feature decoded based on the feature maps. Quantitative and qualitative comparisons of real-world datasets show that the proposed method achieves state-of-the-art leukocyte segmentation performance in terms of both accuracy and robustness.
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References
Zheng, X., Wang, Y., Wang, G., et al.: A novel algorithm based on visual saliency attention for localization and segmentation in rapidly-stained leukocyte images. Micron 56, 17–28 (2014)
Zheng, X., Wang, Y., Wang, G., et al.: Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107, 55–71 (2018)
Huang, D.C., Hung, K.D., Chan, Y.K.: A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J. Syst. Softw. 85(9), 2104–2118 (2012)
Zhang, C., Xiao, X., Li, X., et al.: White blood cell segmentation by color-space-based k-means clustering. Sensors 14(9), 16128–16147 (2014)
Theera-Umpon, N.: White blood cell segmentation and classification in microscopic bone marrow images. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 787–796. Springer, Heidelberg (2005). https://doi.org/10.1007/11540007_98
Kong, H., Gurcan, M., Belkacem-Boussaid, K.: Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans. Med. Imaging 30(9), 1661–1687 (2011)
Song, Y., Cai, W., Huang, H., et al.: Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling. BMC Bioinform. 14(1), 173 (2013)
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. In: Navab, N., Hornegger, J., Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Fan, H., Zhang, F., Xi, L., et al.: LeukocyteMask: an automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J. Biophotonics, e201800488 (2019)
Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327 (1977)
Yasnoff, W.A., Mui, J.K., Bacus, J.W.: Error measures for scene segmentation. Pattern Recognit. 9(4), 217–231 (1977)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)
Fleiss, J.L., Cohen, J., Everitt, B.S.: Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 72(5), 323 (1969)
Zheng, X., Wang, Y., Wang, G.: White blood cell segmentation using expectation-maximization and automatic support vector machine learning in Chinese. Data Acquis. Process. 28, 614–619 (2013)
Dong, G.G.C.: Flexible combination segmentation algorithm for leukocyte images. Chin. J. Sci. Instrum. 9, 1977–1981 (2008)
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (61972187, 61772254), Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), Fujian Provincial Leading Project (2017H0030).
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Lu, Y., Fan, H., Li, Z. (2019). Leukocyte Segmentation via End-to-End Learning of Deep Convolutional Neural Networks. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_16
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DOI: https://doi.org/10.1007/978-3-030-36189-1_16
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