compendium

M. T. McCann, J. Majumdar, C. Peng, C. A. Castro, and J. Kovačević. Algorithm and benchmark dataset for stain separation in histology images. In Proc. IEEE Int. Conf. Image Process., Paris, Oct. 2014.

[ pdf | @ IEEE Xplore | bibtex]

In this work, we present a new algorithm and benchmark dataset for stain separation in histology images. Histology is a critical and ubiquitous task in medical practice and research, serving as a gold standard of diagnosis for many diseases. Automating routine histology analysis tasks could reduce health care costs and improve diagnostic accuracy. One challenge in automation is that histology slides vary in their stain intensity and color; we therefore seek a digital method to normalize the appearance of histology images. As histology slides often have multiple stains on them that must be normalized independently, stain separation must occur before normalization. We propose a new digital stain separation method for one common staining type, hematoxylin and eosin staining; this method improves on the state-of-the-art by adjusting the contrast of its E-only estimate and including a notion of stain interaction. To validate this method, we have collected a new benchmark dataset containing ground truth images for stain separation via chemical destaining, which we release publicly. Our experiments show that our method achieves more accurate stain separation than two comparison methods and that this improvement in separation accuracy leads to improved normalization.

The archive contains the code for our stain separation method as well as the benchmark dataset used in the paper.

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This work is licensed under a Creative Commons GNU General Public License. To view a copy of this license, visit http://creativecommons.org/licenses/GPL/2.0. If you use this code or any part thereof in your research or publication, please also include a reference to this paper. Thank you!

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