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Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

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Book cover Deep Learning and Convolutional Neural Networks for Medical Image Computing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE) trained with structured labels and discriminative losses. The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of bases in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained on the original data with their structured labels and discriminative losses, is used for cell segmentation . To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with both structured labels and discriminative losses to cell detection and segmentation. It is observed that structured learning can effectively handle weak or misleading edges, and discriminative training encourages the model to learn groups of filters that activate simultaneously for different input images to ensure better segmentation. The proposed method is extensively tested on four data sets containing more than 6000 cells obtained from brain tumor, lung cancer, and breast cancer and neuroendocrine tumor (NET) images. Our algorithm achieves the best performance compared with other state of the arts.

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Author information

Authors and Affiliations

  1. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA

    Hai Su, Xiangfei Kong, Yuanpu Xie & Lin Yang

  2. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

    Fuyong Xing & Lin Yang

  3. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Shaoting Zhang

Authors
  1. Hai Su
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  2. Fuyong Xing
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  3. Xiangfei Kong
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  4. Yuanpu Xie
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  5. Shaoting Zhang
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  6. Lin Yang
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Corresponding author

Correspondence to Lin Yang .

Editor information

Editors and Affiliations

  1. NIH Clinical Center, Bethesda, Maryland, USA

    Le Lu

  2. Siemens Healthcare Technology Center, Princeton, New Jersey, USA

    Yefeng Zheng

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. University of Florida, Gainesville, Florida, USA

    Lin Yang

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Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., Yang, L. (2017). Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Image Computing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-42999-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-42999-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42998-4

  • Online ISBN: 978-3-319-42999-1

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