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International Conference on Information Technologies in Biomedicine

ITIB 2018: Information Technology in Biomedicine pp 379–392Cite as

Semantic Segmentation of Colon Glands in Inflammatory Bowel Disease Biopsies

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 762))

Abstract

Robust delineation of tissue components in hematoxylin and eosin (H&E) stained slides is a critical step in quantifying tissue morphology. Fully convolutional neural networks (FCN) are ideally suited for automatic and efficient segmentation of tissue components in H&E slides. However, their performance relies on the network architecture, quality and depth of training. Here we introduce a set of 802 image tiles of colon biopsies from 2 subjects with inflammatory bowel disease (IBD) annotated for glandular epithelium (EP), gland lumen together with goblet cells (LG), and stroma (ST). We either trained the FCN-8s de-novo on our images (DN-FCN-8s) or pre-trained on the ImageNet dataset and fine-tuned on our images (FT-FCN-8s). For comparison, we used the U-Net trained de-novo. The training involved 700/802 images, leaving 102 images as a testing set. Ultimately, each model was validated in an independent digital biopsy slide. We also determined how the number of images used for training affects the performance of the model and observed a plateau in trainability at 700 images. In the testing set, U-Net and FT-FCN-8s achieved accuracies of 92.30% and 92.26% respectively. In the independent biopsy slide, U-Net demonstrated a segmentation accuracy of 88.64%, with F1-scores of 0.74 (EP), 0.92 (LG), and 0.93 (ST). The performance of the FT-FCN-8s was slightly worse, but the model required fewer images to reach a high classification performance. Our data demonstrate that all 3 FCNs are appropriate for segmentation of glands in biopsies from patients with IBD and open the door for quantification of IBD associated pathologies.

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Acknowledgement

This work was in part funded by seed grants from the Department of Surgery, The Biobank & Translation Research Core at Cedars-Sinai Medical Center, in part by Cedars-Sinai in support of CTSI grant UL1TR001881-01, and in part by the National Science Center (Poland) by the grant UMO-2016/23/N/ST6/02076. The authors would also like to thank The Inflammatory Bowel Disease Consortium for digital slides of colon specimens.

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Authors and Affiliations

  1. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Zhaoxuan Ma, Nathan Ing, Dermot McGovern & Beatrice Knudsen

  2. Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Zhaoxuan Ma & Arkadiusz Gertych

  3. Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Nathan Ing, Hootan Salemi, Beatrice Knudsen & Arkadiusz Gertych

  4. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Dermot McGovern

  5. Faculty of Electrical Engineering, Warsaw Technical University, Warsaw, Poland

    Zaneta Swiderska-Chadaj

Authors
  1. Zhaoxuan Ma
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  2. Zaneta Swiderska-Chadaj
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  3. Nathan Ing
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  4. Hootan Salemi
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  5. Dermot McGovern
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  6. Beatrice Knudsen
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  7. Arkadiusz Gertych
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Corresponding author

Correspondence to Arkadiusz Gertych .

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Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

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Ma, Z. et al. (2019). Semantic Segmentation of Colon Glands in Inflammatory Bowel Disease Biopsies. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_34

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