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Cascade Dense-Unet for Prostate Segmentation in MR Images

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

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Abstract

Automatic prostate segmentation from magnetic resonance images can assist in diagnosis and radiological planning. The extensive clinical application of this task has attracted the attention of researchers. However, due to noise, blurred boundaries and scale variation, it is very challenging to segment prostate from magnetic resonance images. We propose a cascade method for prostate segmentation. The model consists of two stage. In the first stage, a dense-unet model are used to obtain the initial segmentation results. In the second stage, the segmentation result of the first stage is used as prior knowledge, and another dense-unet is used to obtain more accurate segmentation results. The experimental results show that the proposed method can obtain more accurate segmentation results.

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Acknowledgement

This work is supported by Shanghai Science and Technology Commission (grant No. 17511104203) and NSFC (grant NO. 61472087).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 201203, China

    Suiyi Li, Yuxuan Chen, Su Yang & Wuyang Luo

Authors
  1. Suiyi Li
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  2. Yuxuan Chen
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  3. Su Yang
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  4. Wuyang Luo
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Corresponding author

Correspondence to Su Yang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Li, S., Chen, Y., Yang, S., Luo, W. (2019). Cascade Dense-Unet for Prostate Segmentation in MR Images. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_46

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  • DOI: https://doi.org/10.1007/978-3-030-26763-6_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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