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APU-Net: An Attention Mechanism Parallel U-Net for Lung Tumor Segmentation.

Lung cancer is one of the malignant tumors with high morbidity and mortality, and lung nodules are the early stages of lung cancer. The symptoms of pulmonary nodules are not obvious in the clinic, and the optimal treatment time is missed due to the missed diagnosis in the clinic. A parallel U-Net ne...

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Published in:BioMed Research International pp. 1 - 16
Main Authors: Zhou, Tao, Dong, YaLi, Lu, HuiLing, Zheng, XiaoMin, Qiu, Shi, Hou, SenBao
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 5/9/2022
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
      issn: 23146133
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      dt: 5/9/2022
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        156763005
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        10.1155/2022/5303651
        156763005
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        atl: APU-Net: An Attention Mechanism Parallel U-Net for Lung Tumor Segmentation.
      aug:
        au:
          Zhou, Tao
          Dong, YaLi
          Lu, HuiLing
          Zheng, XiaoMin
          Qiu, Shi
          Hou, SenBao
        affil: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia 750021, China
      sug:
        subj:
          Lung Neoplasms Diagnosis
          Lung Neoplasms Pathology
          Lung Neoplasms Classification
          Image Processing, Computer Assisted Methods
          Radiographic Image Interpretation, Computer-Assisted Methods
          Human
          Positron-Emission Tomography
          Tomography, X-Ray Computed
          Failure to Diagnose
          Neural Networks (Computer) Methods
      ab: Lung cancer is one of the malignant tumors with high morbidity and mortality, and lung nodules are the early stages of lung cancer. The symptoms of pulmonary nodules are not obvious in the clinic, and the optimal treatment time is missed due to the missed diagnosis in the clinic. A parallel U-Net network called APU-Net is proposed. Firstly, two parallel U-Net networks are used to extract the features of different modalities. Among them, the subnetwork UNet_B extracts the CT image features, and the subnetwork UNet_A consists of two encoders to extract the PET/CT and PET image features. Secondly, multimodal feature extraction blocks are used to extract features for PET/CT and PET images in UNet_B network. Thirdly, a hybrid attention mechanism is added to the encoding paths of the UNet_A and UNet_B. Finally, a multiscale feature aggregation block is used for extracting feature maps of different scales of decoding path. On the lung tumor 18FDGPET/CT multimodal medical images dataset, experiments' results show that the DSC, Recall, VOE, and RVD coefficients of APU-Net are 96.86%, 97.53%, 3.18%, and 3.29%, respectively. APU-Net can improve the segmentation accuracy of the adhesion between the lesion of complex shape and the normal tissue. This has positive significance for computer-aided diagnosis.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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