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Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms

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Deep Learning and Convolutional Neural Networks for Medical Image Computing

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

Abstract

The segmentation of masses from mammogram is a challenging problem because of their variability in terms of shape, appearance and size, and the low signal-to-noise ratio of their appearance. We address this problem with structured output prediction models that use potential functions based on deep convolution neural network (CNN) and deep belief network (DBN). The two types of structured output prediction models that we study in this work are the conditional random field (CRF) and structured support vector machines (SSVM). The label inference for CRF is based on tree re-weighted belief propagation (TRW) and training is achieved with the truncated fitting algorithm; whilst for the SSVM model, inference is based upon graph cuts and training depends on a max-margin optimization. We compare the results produced by our proposed models using the publicly available mammogram datasets DDSM-BCRP and INbreast, where the main conclusion is that both models produce results of similar accuracy, but the CRF model shows faster training and inference. Finally, when compared to the current state of the art in both datasets, the proposed CRF and SSVM models show superior segmentation accuracy.

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Acknowledgements

This work was partially supported by the Australian Research Council’s Discovery Projects funding scheme (project DP140102794). Prof. Bradley is the recipient of an Australian Research Council Future Fellowship(FT110100623).

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Authors and Affiliations

  1. Australian Centre for Visual Technologies, The University of Adelaide, Adelaide, SA, Australia

    Neeraj Dhungel & Gustavo Carneiro

  2. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia

    Andrew P. Bradley

Authors
  1. Neeraj Dhungel
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  2. Gustavo Carneiro
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  3. Andrew P. Bradley
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Corresponding author

Correspondence to Neeraj Dhungel .

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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Dhungel, N., Carneiro, G., Bradley, A.P. (2017). Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-42999-1_13

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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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