Format

Send to

Choose Destination
Med Image Anal. 2019 Aug 21;58:101547. doi: 10.1016/j.media.2019.101547. [Epub ahead of print]

Learning to detect lymphocytes in immunohistochemistry with deep learning.

Author information

1
Department of Pathology, Radboud University Medical Center, The Netherlands. Electronic address: zaneta.swiderska@radboudumc.nl.
2
Department of Pathology, Radboud University Medical Center, The Netherlands.
3
Department of Pathology, Radboud University Medical Center, The Netherlands; Department of Clinical Medicine, Aarhus University, Denmark; Institute of Pathology, Randers Regional Hospital, Denmark.
4
Department of Pathology, University Medical Center, Utrecht, The Netherlands.
5
Mayo Clinic, Jacksonville, Florida, USA.
6
Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal.
7
Fiona Stanley Hospital, Murdoch, Perth, Western Australia.
8
Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.
9
Department of Pathology, Radboud University Medical Center, The Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.

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 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.

KEYWORDS:

Computational pathology; Deep learning; Immune cell detection; Immunohistochemistry

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center