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Home > Archive>Volume 33, Issue 5, 2022 >1947-1958. DOI:10.13328/j.cnki.jos.006198
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Pancreas Segmentation Based on Dual-decoding U-Net
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TP391

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    Abstract:

    Pancreas segmentation in computed tomography (CT) is one of the most challenging tasks in medical image analysis. Due to small size and changeable shape, the traditional automatic segmentation methods can not achieve the acceptable segmentation accuracy. By using the idea of high-level semantic features to guide low-level features, this study proposes a single-stage pancreas segmentation model based on dual-decoding U-net. The proposed architecture consists of one encoder and two decoders, which can effectively combine low-level spatial information with high-level semantic information using the features of different coding depths to improve the segmentation accuracy of CT slices without clipping and resolution reduction. The experimental results show that this method can achieve better segmentation performance under full-size input. Moreover, the segmentation result by the proposed method is superior to the single-stage methods on the open dataset for pancreas segementation tasks.

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毕秀丽,陆猛,肖斌,李伟生.基于双解码U型卷积神经网络的胰腺分割.软件学报,2022,33(5):1947-1958

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History
  • Received:May 16,2020
  • Revised:August 29,2020
  • Online: May 09,2022
  • Published: May 06,2022
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