Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography

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概要

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• This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boostand extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.

著者

• Shimizu Akinobu

  Tokyo University Of Agriculture And Technology

• Kobatake Hidefumi

  Tokyo Univ. Agriculture & Technol. Tokyo Jpn

• Shimizu Akinobu

  Tokyo University of Agriculture & Technology

• SHINOZAKI Kenji

  Kyushu Cancer Center

• FURUKAWA Daisuke

  Tokyo University of Agriculture and Technology

• NARIHIRA Takuya

  Tokyo University of Agriculture and Technology

• NAWANO Shigeru

  International University of Health and Welfare

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