由于肺部CT图像的特征信息复杂度较高，经典 3D U-Net网络在肺结节分割方面准确率较低，存在误分割等问题。基于此，提出一种基于改进 3D U-Net的网络模型。通过将加入了密集块的 3D U-Net网络和双向特征网络(Bi-FPN)融合，提高了模型分割精度。同时采用深度监督训练机制，进一步提高了网络性能。在公开数据集LUNA-16 上对模型进行比较实验和评估，结果显示，改进后的 3D U-Net网络，Dice相似系数较原模型提高 4%，分割精度为93。9%，敏感度为94。3%，证明该模型在肺结节分割精度及准确率方面具有一定的应用价值。
Research on Lung Nodule Segmentation Method Based on Improved 3D U-Net Model
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.
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