Abstract
Robustness against variations in color and resolution of digitized whole-slide images (WSIs) is an essential requirement for any computer-aided analysis in digital pathology. One common approach to encounter a lack of heterogeneity in the training data is data augmentation. We investigate the impact of different augmentation techniques for whole-slide cartography in colon cancer histology using a newly created multi-scanner database of 39 slides each digitized with six different scanners. A state of the art convolutional neural network (CNN) is trained to differentiate seven tissue classes. Applying a model trained on one scanner to WSIs acquired with a different scanner results in a significant decrease in classification accuracy. Our results show that the impact of resolution variations is less than of color variations: the accuracy of the baseline model trained without any augmentation at all is 73% for WSIs with similar color but different resolution against 35% for WSIs with similar resolution but color deviations. The grayscale model shows comparatively robust results and evades the problem of color variation. A combination of multiple color augmentations methods lead to a significant overall improvement (between 33 and 54 percentage points). Moreover, fine-tuning a pre-trained network using a small amount of annotated data from new scanners benefits the performance for these particular scanners, but this effect does not generalize to other unseen scanners.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bejnordi BE, Litjens G, et al. Stain specific standardization of whole-slide histopathological images. IEEE Trans Med Imaging. 2016;35(2):404–415.
Tellez D, Litjens G, Bàndi P, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal. 2019;58:51624.
Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images. IEEE Comput Graph Appl. 2001;21(5):34–41.
Khan AM, Rajpoot N, Treanor D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng. 2014;61(6):1729–1738.
Zanjani FG, et al. Stain normalization of histopathology images using generative adversarial networks. Proc IEEE Int Symp Biomed Imaging. 2018; p. 573–577.
Tellez D, Balkenhol M, Otte-Höller I, et al. Whole-slide mitosis detection in H E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans Med Imaging. 2018;37(9):2126–2136.
Leo P, Lee G, Shih NNC, et al. Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images. J Med Imaging (Bellingham). 2016;3(4):111.
Chollet F. Xception: deep learning with depthwise separable convolutions. Conf Comput Vis Pattern Recognit. 2017; p. 1800–1807.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Kuritcyn, P. et al. (2021). Robust Slide Cartography in Colon Cancer Histology. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_54
Download citation
DOI: https://doi.org/10.1007/978-3-658-33198-6_54
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-33197-9
Online ISBN: 978-3-658-33198-6
eBook Packages: Computer Science and Engineering (German Language)