Learning to detect lymphocytes in immunohistochemistry with deep learning
Swiderska-Chadaj, Zaneta; Pinckaers, Hans; van Rijthoven, Mart; Balkenhol, Maschenka; Melnikova, Margarita; Geessink, Oscar; Manson, Quirine; Sherman, Mark; Polonia, Antonio; Parry, Jeremy; Abubakar, Mustapha; Litjens, Geert; van der Laak, Jeroen; Ciompi, Francesco
(2019) Medical Image Analysis, volume 58
(Article)
Abstract
The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3+ and CD8+ cells, which we used to train deep learning algorithms for
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automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (κ=0.72), whereas the average pathologists agreement with reference standard was κ=0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org.
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Keywords: Computational pathology, Deep learning, Immune cell detection, Immunohistochemistry, Radiological and Ultrasound Technology, Radiology Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, Health Informatics, Computer Graphics and Computer-Aided Design
ISSN: 1361-8415
Publisher: Elsevier
Note: Funding Information: The authors would like to thank Sophie van den Broek for her support in the process of lymphocyte annotation. This project was supported by the Alpe d’HuZes/Dutch Cancer Society Fund (Grant Number: KUN 2014–7032, KUN 2015–7970), the Netherlands Organization for Scientific Research (NWO) (project number 016.186.152 ), the Stichting IT Projecten (project PATHOLOGIE 2), and partly funded by the European Union ’s Horizon 2020 research and innovation programme under grant agreement No 825292 (ExaMode, http://www.examode.eu/ ). Funding Information: The authors would like to thank Sophie van den Broek for her support in the process of lymphocyte annotation. This project was supported by the Alpe d'HuZes/Dutch Cancer Society Fund (Grant Number: KUN 2014?7032, KUN 2015?7970), the Netherlands Organization for Scientific Research (NWO) (project number 016.186.152), the Stichting IT Projecten (project PATHOLOGIE 2), and partly funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 825292 (ExaMode, http://www.examode.eu/). Publisher Copyright: © 2019 Elsevier B.V.
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