中国医疗设备2024，Vol.39Issue(5)：31-36,98,7.DOI:10.3969/j.issn.1674-1633.2024.05.006
基于U-Net融合Transformer的肺结节分割方法研究
Research on Pulmonary Nodule Segmentation Method Based on U-Net Combined with Transformer
李晓东 1丁鹏2
作者信息
- 1. 山东中医药大学智能与信息工程学院,山东 济南 250355
- 2. 山东中医药大学第二附属医院 后勤党总支,山东 济南 250001
- 折叠
摘要
Abstract
Objective To propose a pulmonary nodule segmentation model to realize pulmonary nodule segmentation.Methods The encoder,hole convolution and Swin Transformer module were added to the U-Net neural network,and a model combining hole convolution,encoder and attention mechanism was proposed,and the performance of the model was verified on LUNA16 public data set.Results The accuracy(ACC),specificity(SP),intersection over union(IOU)and Dice similarity coefficient(DSC)of the segmentation results of this model on LUNA16 public dataset were 0.9651,0.9572,0.8354 and 0.8971,respectively.Conclusion The segmentation model has an excellent performance in ACC,SP,IOU and DSC,can assist doctors to diagnose,and has certain reference value in clinical pulmonary nodule segmentation.关键词
肺结节/肺结节分割/U-Net神经网络/Swin Transformer模块Key words
pulmonary nodule/pulmonary nodule segmentation/U-Net neural network/Swim Transformer module分类
信息技术与安全科学引用本文复制引用
李晓东,丁鹏..基于U-Net融合Transformer的肺结节分割方法研究[J].中国医疗设备,2024,39(5):31-36,98,7.
