Abstract
Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is applied in order to focus on the information content available on pixel-level and to avoid any bias which might be introduced by more elaborated techniques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow.
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We use the provided PyTorch reference implementation [12].
References
Barker, J., Hoogi, A., Depeursinge, A., Rubin, D.L.: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. 30, 60–71 (2016)
BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 460–468. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_53
Gadermayr, M., Appel, V., Klinkhammer, B.M., Boor, P., Merhof, D.: Which way round? A study on the performance of stain-translation for segmenting arbitrarily dyed histological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 165–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_19
Gadermayr, M., Dombrowski, A.K., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting sparse objects in gigapixel whole slide images. Comput. Med. Imaging Graph. 71, 40–48 (2019)
Gadermayr, M., Eschweiler, D., Jeevanesan, A., Klinkhammer, B.M., Boor, P., Merhof, D.: Segmenting renal whole slide images virtually without training data. Comput. Biol. Med. 90, 88–97 (2017)
Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the International Conference on Computer Vision (CVPR 2016) (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR 2017) (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
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), pp. 1107–1110 (2009). https://doi.org/10.1109/ISBI.2009.5193250
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
Sertel, O., Kong, J., Shimada, H., Catalyurek, U.V., Saltz, J.H., Gurcan, M.N.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit. 42(6), 1093–1103 (2009)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the International Conference on Computer Vision (ICCV 2017) (2017)
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Gadermayr, M., Klinkhammer, B.M., Boor, P. (2019). Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_5
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