现代信息科技2024，Vol.8Issue(13)：52-55,60,5.DOI:10.19850/j.cnki.2096-4706.2024.13.011
基于改进3D U-Net模型的肺结节分割方法研究
Research on Lung Nodule Segmentation Method Based on Improved 3D U-Net Model
摘要
Abstract
Due to the high complexity of feature information in lung CT images,the classic 3D U-Net network exhibits low accuracy in lung nodule segmentation,leading to issues such as miss segmentation.To address this,a network model based on improved 3D U-Net is proposed.This model integrates 3D U-Net network with dense blocks with the Bidirectional Feature Pyramid Network(Bi-FPN)to improve the model's segmentation accuracy.The adoption of deep supervision training mechanism further enhances network performance.Comparative experiments and evaluations are conducted on the public dataset LUNA-16,and the results show that the improved 3D U-Net network has a 4%increase in Dice similarity coefficient,a segmentation accuracy of 93.9%,and a sensitivity of 94.3%compared to the original model.This proves that the model has certain application value in the accuracy and precision of lung nodule segmentation.关键词
肺结节分割/CT/3D U-Net/双向特征网络/深度监督Key words
lung nodule segmentation/CT/3D U-Net/bi-directional feature network/Deep Supervision分类
信息技术与安全科学引用本文复制引用
石征锦,李文慧,高天..基于改进3D U-Net模型的肺结节分割方法研究[J].现代信息科技,2024,8(13):52-55,60,5.
