Abstract
Stain color variation in histological images, caused by a variety of factors, is a challenge not only for the visual diagnosis of pathologists but also for cell segmentation algorithms. To eliminate the color variation, many stain normalization approaches have been proposed. However, most were designed for hematoxylin and eosin staining images and performed poorly on immunohistochemical staining images. Current cell segmentation methods systematically apply stain normalization as a preprocessing step, but the impact brought by color variation has not been quantitatively investigated yet. In this paper, we produced five groups of NeuN staining images with different colors. We applied a deep learning image-recoloring method to perform color transfer between histological image groups. Finally, we altered the color of a segmentation set and quantified the impact of color variation on cell segmentation. The results demonstrated the necessity of color normalization prior to subsequent analysis.
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References
Afifi, M., Brubaker, M.A., Brown, M.S.: HistoGAN: controlling colors of GAN-generated and real images via color histograms. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7941–7950 (2021)
BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37(3), 792–802 (2017)
Coltuc, D., Bolon, P., Chassery, J.M.: Exact histogram specification. IEEE Trans. Image Process. 15(5), 1143–1152 (2006)
Cui, Y., Zhang, G., Liu, Z., Xiong, Z., Hu, J.: A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med. Biol. Eng. Comput. 57(9), 2027–2043 (2019). https://doi.org/10.1007/s11517-019-02008-8
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
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)
Liu, D., Zhang, D., Song, Y., Huang, H., Cai, W.: Panoptic feature fusion net: a novel instance segmentation paradigm for biomedical and biological images. IEEE Trans. Image Process. 30, 2045–2059 (2021)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 1107–1110. IEEE (2009)
Mocnik, F.B.: Benford’s law and geographical information-the example of openstreetmap. Int. J. Geograph. Inf. Sci. 35(9), 1746–1772 (2021)
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41 (2001)
Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 953–956. IEEE (2019)
Tellez, D., et al.: Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. 58, 101544 (2019)
Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016)
Wu, H., Souedet, N., Jan, C., Clouchoux, C., Delzescaux, T.: A general deep learning framework for neuron instance segmentation based on efficient UNet and morphological post-processing. arXiv preprint arXiv:2202.08682 (2022)
Wu, H., Wang, Z., Song, Y., Yang, L., Qin, J.: Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11666–11675 (2022)
Acknowledgements.
This work was supported by DIM ELICIT grants from Région Ile-de-France, by the French National Research Agency (project SUMMIT ANR-21-CE45-0022-01) and by the European Union’s Horizon 2020 research and innovation program under the grant agreement No. 945539 (Human Brain Project SGA3).
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Wu, H., Souedet, N., Mabillon, C., Jan, C., Clouchoux, C., Delzescaux, T. (2022). Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation. In: Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (eds) Medical Optical Imaging and Virtual Microscopy Image Analysis. MOVI 2022. Lecture Notes in Computer Science, vol 13578. Springer, Cham. https://doi.org/10.1007/978-3-031-16961-8_11
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DOI: https://doi.org/10.1007/978-3-031-16961-8_11
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