Stain normalization is one of the main tasks in the processing pipeline of computer-aided diagnosis systems in modern digital pathology. Some of the challenges in this tasks are memory and runtime bottlenecks associated with large image datasets. In this work, we present a scalable and fast pipeline for stain normalization using a state-of-the-art unsupervised method based on stain-vector estimation. The proposed system supports single-node and distributed implementations. Based on a highly-optimized engine, our architecture enables high-speed and large-scale processing of high-magnification whole-slide images (WSI). We demonstrate the performance of the system using measurements from different datasets. Moreover, by using a novel pixel-sampling optimization we show lower processing time per image than the scanning time of ultrafast WSI scanners with the single-node implementation and additional 3.44 average speed-up with the 4-nodes distributed pipeline.
CITATION STYLE
Stanisavljevic, M., Anghel, A., Papandreou, N., Andani, S., Pati, P., Rüschoff, J. H., … Pozidis, H. (2019). A fast and scalable pipeline for stain normalization of whole-slide images in histopathology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11134 LNCS, pp. 424–436). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_32
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