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.
Keywords
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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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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