Abstract
Tissue composition plays an essential role in diagnosis and prognosis of colorectal cancer (CRC). Studies have shown that the relative proportion of tissue composition on colorectal specimens is potentially prognostic of outcome in CRC patients. Some of the important tissue partitions include blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. A challenge in accurately determining quantitative measurements of tissue composition however is in the need for automated tissue partitioning image analysis tools. Towards this goal, we present a Deeptissue Net, a deep learning strategy which involves integrating DenseNet with Focal Loss. In order to show the effectiveness of Deeptissue Net, the model was trained with 40 WSIs from one site and tested on 620 WSIs from two sites. 10 distinct tissue partitions are blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. The ground truth for training and evaluating Deeptissue Net involved careful annotation of the different tissue compartments by expert pathologists. The Deeptissue net was trained with the tissue partitions delineated for the 10 classes on the 40 WSIs and subsequently evaluated on the remaining \(N=620\) datasets. By measuring with confusion matrices, the Deeptissue Net achieves the accuracy of 0.72, 0.84, and 0.88 in classifying mucus, stroma, and necrosis on the 2nd batch of Dataset 1; 0.85 and 0.96 in classifying mucus and muscle on Dataset 2, respectively, which significantly outperformed DenseNet and ResNet.
J. Xu, C. Cai, Y. Zhou and B. Yao—are the joint first authors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelsamea, M.M., et al.: A cascade-learning approach for automated segmentation of tumour epithelium in colorectal cancer. ESA 118, 539–552 (2019)
Bianconi, F., et al.: Discrimination between tumour epithelium and stroma via perception-based features. Neurocomputing 154, 119–126 (2015)
Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018)
Cruz-Roa, A., et al.: Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. NSR 7, 46450 (2017)
Cruz-Roa, A., et al.: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection. PLOS One 13(5), e0196828 (2018)
He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. JPI 7(1), 29–29 (2016)
Kather, J.N., et al.: Multi-class texture analysis in colorectal cancer histology. NSR 6, 27988 (2016)
Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLOS Med. 16(1), e1002730 (2019)
Lin, T.Y., et al.: Focal loss for dense object detection. TPAMI (2018)
Linder, N., et al.: Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn. Pathol. 7(1), 22 (2012)
Magee, D., et al.: Colour normalisation in digital histopathology images (2009)
Nirschl, J.J., et al.: A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLOS One 13(4), e0192726 (2018)
Pierorazio, P.M., Walsh, P.C., Partin, A.W., Epstein, J.I.: Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int. (2019)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Sirinukunwattana, K., et al.: Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer. NSR 8(1), 13692 (2018). Sep
Xu, J., et al.: A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191, 214–223 (2016)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, J. et al. (2019). Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-23937-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23936-7
Online ISBN: 978-3-030-23937-4
eBook Packages: Computer ScienceComputer Science (R0)