基于深度迁移学习的肺结节分割方法

针对U-Net分割小体积肺结节效果较差的问题，提出一种基于深度迁移学习的分割方法，利用分块式叠加微调（BSFT）策略辅助分割肺结节。首先，利用卷积神经网络学习自然图像大数据集的特征信息；然后，将所学特征迁移到进行肺结节图像小数据集分割的网络，从该网络最后一个下采样层开始逐块释放、微调训练，直到网络完成最后一层的叠加；最后，定量分析Dice相似性系数，以确定最佳分割网络。实验结果表明，BSFT在LUNA16肺结节公开数据集上的Dice值达到0.917 9，该策略的性能明显优于主流肺结节分割算法。

Pulmonary nodule segmentation method based on deep transfer learning

Focused on the issue that U-Net has a poor segmentation effect for small-volume pulmonary nodules, a segmentation method based on deep transfer learning was proposed, and Block Superimposed Fine-Tuning (BSFT) strategy was used to assist the segmentation of pulmonary nodules. Firstly, convolutional neural network was used to learn the feature information of large natural image datasets. Then, the learned features were transferred to the network for the segmentation of small pulmonary nodule image datasets. From the last sampling layer of the network, the network was released block by block and fine-tuned until the network completed the superimposition of the last layer. Finally, the similarity coefficient of Dice was quantitatively analyzed to determine the optimal segmentation network. The experimental results show that the Dice value of BSFT on LUNA16 pulmonary nodule open dataset reaches 0.917 9, which is obviously better than that of the mainstream pulmonary nodule segmentation algorithms.