Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (3): 76-87.doi: 10.16088/j.issn.1001-6600.2021070402

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Segmentation of Lung Nodules Based on Multi-receptive Field and Grouping Attention Mechanism

ZHANG Ping, XU Qiaozhi*   

  1. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot Inner Mongolia 010022,China
  • Received:2021-07-04 Revised:2021-10-17 Online:2022-05-25 Published:2022-05-27

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Abstract: It is very important for diagnoses and treatments of lung tumors to segment lung nodules from CT images automatically and effectively. However, the lung nodules usually are very small, their shapes are irregular, and sometimes they are very similar to adjacent tissues and organs in vision, which brings difficulties to the segmentation task. This paper proposes a lung nodules segmentation network MRF-GMA based on multi-receptive field and grouped mixed attention mechanism. Firstly, the multi-receptive field feature aggregation module can capture nodules of different scales; secondly, the grouped mixed attention is used to improve the resolution of nodular pixels; finally, the hybrid loss function is used to optimize the training process to alleviate the class imbalance problem. In the experiment, MRF-GMA is respectively compared with FCN, SEGNET, R2U-NET and Attention U-NET, and the results show that MRF-GMA model has the best performance in DSC, Accuracy and Recall, and has increased by 2.25%, 1.19% and 2.98%, respectively, compared with the Attention U-Net model.

Key words: CT image, lung nodules segmentation, deep learning, feature aggregation, attention mechanism

CLC Number: 

  • R734.2
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