Abstract
Due to differences in tissue preparations, staining protocols and scanner models, stain colors of digitized histological images are excessively diverse. Color normalization is almost a necessary procedure for quantitative digital pathology analysis. Though several color normalization methods have been proposed, most of them depend on selection of representative templates and may fail in regions not matching the templates. We propose an enhanced cycle-GAN based method with a novel auxiliary input for the generator by computing a stain color matrix for every H&E image in the training set. The matrix guides the translation in the generator, and thus stabilizes the cycle consistency loss. We applied our proposed method as a pre-processing step for a breast metastasis classification task on a dataset from five medical centers and achieved the highest performance compared to other color normalization methods. Furthermore, our method is template-free and may be applied to other datasets without finetuning.
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References
Bejnordi, B.E., van der Laak, J.: Camelyon16: grand challenge on cancer metastasis detection in lymph nodes 2016 (2017)
BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37(3), 792–802 (2018)
Gavrilovic, M., et al.: Blind color decomposition of histological images. IEEE Trans. Med. Imaging 32(6), 983–994 (2013)
Geessink, O., Bándi, P., Litjens, G., van der Laak, J.: Camelyon17: grand challenge on cancer metastasis detection and classification in lymph nodes (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse autoencoders (stanosa): application to digital pathology. Comput. Med. Imaging Graph 57, 50–61 (2017)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Eng. 61(6), 1729–1738 (2014)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 1107–1110 (2009)
Rabinovich, A., Agarwal, S., Laris, C., Price, J.H., Belongie, S.J.: Unsupervised color decomposition of histologically stained tissue samples. In: Advances in Neural Information Processing Systems, pp. 667–674 (2004)
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics Appl. 21(5), 34–41 (2001)
Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2107–2116 (2017)
Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016)
Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A., de With, P.H.: Stain normalization of histopathology images using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 573–577 (2018)
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Zhou, N., Cai, D., Han, X., Yao, J. (2019). Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_77
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DOI: https://doi.org/10.1007/978-3-030-32239-7_77
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