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Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference

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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

We introduce a new segmentation methodology that combines the structured output inference from deep belief networks and the delineation from level set methods to produce accurate segmentation of anatomies from medical images. Deep belief networks can be used in the implementation of accurate segmentation models if large annotated training sets are available, but the limited availability of such large datasets in medical image analysis problems motivates the development of methods that can circumvent this demand. In this chapter, we propose the use of level set methods containing several shape and appearance terms, where one of the terms consists of the result from the deep belief network. This combination reduces the demand for large annotated training sets from the deep belief network and at the same time increases the capacity of the level set method to model more effectively the shape and appearance of the visual object of interest. We test our methodology on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge dataset and on Japanese Society of Radiological Technology (JSRT) lung segmentation dataset, where our approach achieves the most accurate results of the field using the semi-automated methodology and state-of-the-art results for the fully automated challenge.

This work is an extension of the papers published by the same authors at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) [1] and the IEEE International Conference on Image Processing (ICIP 2015) [2].

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Acknowledgements

This work was partially supported by the Australian Research Council’s Discovery Projects funding scheme (project DP140102794). Tuan Anh Ngo acknowledges the support of the 322 Program - Vietnam International Education Development, Ministry of Education and Training (VIED-MOET).

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information Technology, Vietnam National University of Agriculture, Hanoi, Vietnam

    Tuan Anh Ngo

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

    Gustavo Carneiro

Authors
  1. Tuan Anh Ngo
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  2. Gustavo Carneiro
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Corresponding author

Correspondence to Tuan Anh Ngo .

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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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Ngo, T.A., Carneiro, G. (2017). Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference. 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_12

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  • DOI: https://doi.org/10.1007/978-3-319-42999-1_12

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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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