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Pulmonary nodule segmentation based on REMU-Net

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Abstract

In recent years, U-Net has shown excellent performance in medical image segmentation, but it cannot accurately segment nodules of smaller size when segmenting pulmonary nodules. To make it more accurate to segment pulmonary nodules in CT images, U-Net is improved to REMU-Net. First, ResNeSt, which is the state-of-the-art ResNet variant, is used as the backbone of the U-Net, and a spatial attention module is introduced into the Split-Attention block of ResNeSt to enable the network to extract more diverse and efficient features. Secondly, a feature enhancement module based on the atrous spatial pyramid pooling (ASPP) is introduced in the U-Net, which is utilized to obtain more abundant context information. Finally, replacing the skip connection of the U-Net with a multi-scale skip connection overcomes the limitation that the decoder subnet can only accept same-scale feature information. Experiments show that REMU-Net has a Dice score of 84.76% on the LIDC-IDRI dataset. The network has better segmentation performance than most other existing U-Net improvement networks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51975170), Youth Innovation Fund of Heilongjiang Academy of Sciences (Grant No. CXJQ2020WL01), Basic Applied Technology of Heilongjiang Institutes Research Special Project (Grant No. ZNJZ2020WL01), Natural Science Foundation of Heilongjiang Province (Grant No. LH2019F024).

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51975170), Youth Innovation Fund of Heilongjiang Academy of Sciences (Grant No. CXJQ2020WL01), Basic Applied Technology of Heilongjiang Institutes Research Special Project (Grant No. ZNJZ2020WL01), Natural Science Foundation of Heilongjiang Province (Grant No. LH2019F024).

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Authors and Affiliations

  1. Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, 150040, China

    Dongjie Li & Shanliang Yuan

  2. Heilongjiang Atomic Energy Research Institute, Harbin, 150086, China

    Gang Yao

Authors
  1. Dongjie Li
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  2. Shanliang Yuan
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  3. Gang Yao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by DL, SY, and GY. The first draft of the manuscript was written by SY, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dongjie Li.

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Li, D., Yuan, S. & Yao, G. Pulmonary nodule segmentation based on REMU-Net. Phys Eng Sci Med 45, 995–1004 (2022). https://doi.org/10.1007/s13246-022-01157-9

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  • DOI: https://doi.org/10.1007/s13246-022-01157-9

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