Abstract
Digitized pathological diagnosis has been in increasing demand recently. It is well known that color information is critical to the automatic and visual analysis of pathological slides. However, the color variations due to various factors not only have negative impact on pathologist’s diagnosis, but also will reduce the robustness of the algorithms. The factors that cause the color differences are not only in the process of making the slices, but also in the process of digitization. Different strategies have been proposed to alleviate the color variations. Most of such techniques rely on collecting color statistics to perform color matching across images and highly dependent on a reference template slide. Since the pathological slides between hospitals are usually unpaired, these methods do not yield good matching results. In this work, we propose a novel network that we refer to as Transitive Adversarial Networks (TAN) to transfer the color information among slides from different hospitals or centers. It is not necessary for an expert to pick a representative reference slide in the proposed TAN method. We compare the proposed method with the state-of-the-art methods quantitatively and qualitatively. Compared with the state-of-the-art methods, our method yields an improvement of 0.87 dB in terms of PSNR, demonstrating the effectiveness of the proposed TAN method in stain style transfer.
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References
Basavanhally, A., Madabhushi, A.: EM-based segmentation-driven color standardization of digitized histopathology. In: Medical Imaging 2013: Digital Pathology, vol. 8676, p. 86760G. International Society for Optics and Photonics (2013)
Bautista, P.A., Hashimoto, N., Yagi, Y.: Color standardization in whole slide imaging using a color calibration slide. J. Pathol. Inform. 5, 4 (2014)
Bejnordi, B.E., et al.: Stain specific standardization of whole-slide histopathological images. IEEE Trans. Med. Imaging 35(2), 404–415 (2015)
Bejnordi, B.E., Timofeeva, N., Otte-Höller, I., Karssemeijer, N., van der Laak, J.A.: Quantitative analysis of stain variability in histology slides and an algorithm for standardization. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904108. International Society for Optics and Photonics (2014)
BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37(3), 792–802 (2017)
Ciompi, F., et al.: The importance of stain normalization in colorectal tissue classification with convolutional networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 160–163. IEEE (2017)
Guan, S., Khan, A., Sikdar, S., Chitnis, P.: Fully dense UNet for 2D sparse photoacoustic tomography artifact removal. IEEE J. Biomed. Health Inform. (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Ismail, S.M., et al.: Observer variation in histopathological diagnosis and grading of cervical intraepithelial neoplasia. BMJ 298(6675), 707–710 (1989)
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)
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, Part II. 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)
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint: arXiv:1512.09300 (2015)
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)
Magee, D., et al.: Colour normalisation in digital histopathology images. In: Proceedings of Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (MICCAI Workshop), vol. 100, pp. 100–111. Citeseer (2009)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Niethammer, M., Borland, D., Marron, J.S., Woosley, J., Thomas, N.E.: Appearance normalization of histology slides. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds.) MLMI 2010. LNCS, vol. 6357, pp. 58–66. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15948-0_8
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41 (2001)
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, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. arXiv preprint: arXiv:1804.01601 (2018)
Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted Res-UNet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327–331. IEEE (2018)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgement
This study was supported by the National Natural Science Foundation of China (Grant No. 6190010435) and the Science and Technology Program of Fujian Province, China (Grant No. 2019YZ016006).
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Cai, S. et al. (2019). Stain Style Transfer Using Transitive Adversarial Networks. In: Knoll, F., Maier, A., Rueckert, D., Ye, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2019. Lecture Notes in Computer Science(), vol 11905. Springer, Cham. https://doi.org/10.1007/978-3-030-33843-5_15
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