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Tumor Growth Prediction Using Convolutional Networks

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

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

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Abstract

Prognostic tumor growth modeling via volumetric medical imaging observations is a challenging yet important problem in precision and predictive medicine. It can potentially imply and lead to better outcomes of tumor treatment management and surgical planning. Traditionally, this problem is tackled through mathematical modeling. Recent advances of convolutional neural networks (ConvNets) have demonstrated higher accuracy and efficiency than conventional mathematical models can be achieved in predicting tumor growth. This indicates that deep learning based data-driven techniques may have great potentials on addressing such problem. In this chapter, we first introduce a statistical group learning approach to predict the pattern of tumor growth that incorporates both the population trend and personalized data, where deep ConvNet is used to model the voxel-wise spatiotemporal tumor progression. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal pancreatic tumor data set show that both approaches substantially outperform a state-of-the-art mathematical model-based approach in both accuracy and efficiency.

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Author information

Authors and Affiliations

  1. Nvidia Corporation, Bethesda, MD, 20814, USA

    Ling Zhang

  2. PAII Inc., Bethesda Research Lab, 6720B Rockledge Drive, Ste 410, Bethesda, MD, 20817, USA

    Lu Le

  3. Johns Hopkins University, Baltimore, MD, USA

    Lu Le

  4. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory and Clinical Image Processing Service Radiology and Imaging Sciences Department, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA

    Ronald M. Summers

  5. Endocrine Oncology Branch National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    Electron Kebebew

  6. Tencent Holdings Limited, Shenzhen, 518057, China

    Jianhua Yao

Authors
  1. Ling Zhang
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  2. Lu Le
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  3. Ronald M. Summers
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  4. Electron Kebebew
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  5. Jianhua Yao
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Corresponding author

Correspondence to Ling Zhang .

Editor information

Editors and Affiliations

  1. Bethesda Research Lab, PAII Inc., Bethesda, MD, USA

    Le Lu

  2. Nvidia Corporation, Bethesda, MD, USA

    Xiaosong Wang

  3. School of Computer Science, University of Adelaide, Adelaide, SA, Australia

    Gustavo Carneiro

  4. Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Lin Yang

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Zhang, L., Le, L., Summers, R.M., Kebebew, E., Yao, J. (2019). Tumor Growth Prediction Using Convolutional Networks. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-13969-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-13969-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13968-1

  • Online ISBN: 978-3-030-13969-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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