A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology

Milos Stanisavljevic, Andreea Anghel, Nikolaos Papandreou, Sonali Andani, Pushpak Pati, Jan Hendrik Ruschoff, Peter Wild, Maria Gabrani, Haralampos Pozidis; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract


Stainnormalizationisoneofthemaintasksintheprocessing 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 largescale 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Stanisavljevic_2018_ECCV_Workshops,
author = {Stanisavljevic, Milos and Anghel, Andreea and Papandreou, Nikolaos and Andani, Sonali and Pati, Pushpak and Hendrik Ruschoff, Jan and Wild, Peter and Gabrani, Maria and Pozidis, Haralampos},
title = {A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}