Abstract
We present Icytomine, a user-friendly software platform for processing large images from slide scanners. Icytomine integrates in one unique framework the tools and algorithms that were developed independently on Icy and Cytomine platforms to visualise and process digital pathology images. We illustrate the power of this new platform through the design of a dedicated program that uses convolutional neural network to detect and classify glomeruli in kidney biopsies coming from a multicentric clinical study. We show that by streamlining the analytical capabilities of Icy with the AI tools found in Cytomine, we achieved highly promising results.
We acknowledge the Inception program (Investissement d’Avenir grant ANR-16-CONV-0005) for providing their GPU lab for our research.
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References
Fuchs, T.J., Buhmann, J.M.: Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35(7–8), 515–530 (2011)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Deroulers, C., Ameisen, D., Badoual, M., Gerin, C., Granier, A., Lartaud, M.: Analyzing huge pathology images with open source software. Diagn. Pathol. 8(1), 1–8 (2013)
Nelissen, B.G.L., Van Herwaarden, J.A., Moll, F.L., Van Diest, P.J., Pasterkamp, G.: SlideToolkit: an assistive toolset for the histological quantification of whole slide images. PLoS One 9(11), e110289 (2014)
Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 16878 (2017)
Della Mea, V., Baroni, G.L., Pilutti, D., Di Loreto, C.: SlideJ: an ImageJ plugin for automated processing of whole slide images. PLoS One 12, e0180540 (2017)
Marée, R., Dallongeville, S., Olivo-Marin, J.-C., Meas-Yedid, V.: An approach for detection of glomeruli in multisite digital pathology, pp. 1033–1036, April 2016
de Chaumont, F., et al.: ICY: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9(7), 690–6 (2012)
Marée, R., et al.: Collaborative analysis of multi-gigapixel imaging data using cytomine. Bioinformatics 32(9), 1395–1401 (2016)
Marsh, J.N., et al.: Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans. Med. Imaging 37, 1 (2018)
Gadermayr, M., Dombrowski, A.-K., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting whole slide images of the kidney. CoRR, vol. abs/1708.00251 (2017)
Gadermayr, M., Eschweiler, D., Jeevanesan, A., Klinkhammer, B.M., Boor, P.: Segmenting renal whole slide images virtually without training data. Comput. Biol. Med. 90, 88–97 (2017)
Fitzgibbon, A.W., Pilu, M., Fisher, R.B.: Direct least-squares fitting of ellipses. IEEE Trans. PAMI 21(5), 476–480 (1999). May
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 2004 (2004)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision. ArXiv e-prints, December 2015
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. ArXiv e-prints, December 2015
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ArXiv e-prints, September 2014
He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. In: 2017 IEEE ICCV, pp. 2980–2988 (2017)
Abdulla, W.: Mask R-CNN for object detection and instance segmentation on keras and tensorflow (2017). https://github.com/matterport/Mask_RCNN
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Gonzalez Obando, D.F., Mandache, D., Olivo-Marin, JC., Meas-Yedid, V. (2019). Icytomine: A User-Friendly Tool for Integrating Workflows on Whole Slide Images. 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_21
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DOI: https://doi.org/10.1007/978-3-030-23937-4_21
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