基于3D ResUnet网络的肺结节分割

目的:将深度残差结构和U-Net网络结合形成新的网络ResUnet，并利用ResUnet深度学习网络结构对胸部CT影像进行图像分割以提取肺结节区域。方法:使用的CT影像数据来源于LUNA16数据集，首先对CT图像预处理提取出肺实质，然后对其截取立体图像块并进行数据增强来扩充样本数，形成相应的肺结节掩膜图像，最后将生成的样本输入到ResUnet模型中进行训练。结果:本研究模型最终的精度和召回率分别为35.02%和97.68%。结论:该模型能自动学习肺结节特征，为后续的肺癌自动诊断提供可靠基础，减少临床诊断的成本并节省医生诊断的时间。 【关键词】肺结节；分割；深度残差结构；召回率；ResUnet

Lung nodule segmentation based on 3D ResUnet network

Objective To propose a novel network ResUnet by combining deep residual structure with U-Net network, and to extract lung nodule region by segmenting the chest CT image with the use of ResUnet deep learning network structure. Methods The CT image data used in the study were derived from LUNA16 dataset. The lung parenchyma was firstly extracted from CT image preprocessing, and then, the stereo image block was intercepted and the simple size was expanded by data enhancement, thereby obtaining the corresponding lung nodule mask image. Finally, the obtained simple were imported into ResUnet model for training. Results The final accuracy and recall rate of the proposed model were 35.02% and 97.68%, respectively. Conclusion The proposed model can automatically learn the characteristics of pulmonary nodules and provide a reliable basis for the subsequent automatic diagnosis of lung cancer, thus reducing the cost of clinical diagnosis and shortening the time for diagnosis.