基于生成对抗网络的鼻咽癌靶区自动勾画研究

Research on automatic delineation of nasopharyngeal carcinoma target area based on generative adversarial network

目的评估基于一种深度学习网络模型2D-PE-GAN的鼻咽癌靶区自动勾画模型, 对靶区勾画的工作效率的提高作用。方法模型采用生成对抗网络的架构, 生成器采用UNet相似结构, 并在生成器的每一层卷积操作后添加2D-PE-block, 提升勾画准确度。实验数据使用130例鼻咽癌CT图像, 模型训练前对图像进行预处理, 通过对比UNet、GAN, 以及添加注意力机制的GAN三种模型, 使用Dice系数、豪斯多夫距离、准确率、马修斯相关系数、杰卡德距离, 说明提出模型的有效性。结果相比于UNet、GAN、加入注意力机制的GAN, 2D-PE-GAN网络分割CTV的Dice系数平均值提高了26%、4%、2%, 分割GTV的Dice系数平均值提高21%、4%、2%。相比于加入注意力机制的GAN, 2D-PE-GAN的参数和时间分别减少了0.16%、18%。结论与UNet、GAN、加入注意力机制的GAN三种模型相比, 2D-PE-GAN用于鼻咽癌靶区勾画, 分割准确度均有所提升, 同时, 与提出原因相似的注意力机制相比, 使用2D-PE-GAN在分割准确度相差不大的情况下, 能减少计算资源的占用。

Objective To propose a deep learning network model 2D‐PE‐GAN to automatically delineate the target area of nasopharyngeal carcinoma and improve the efficiency of target area delineation.Methods The model adopted the architecture of generative adversarial networks which used a UNet similar structure as the generator,and 2D‐PE‐block was added after each layer of convolution operation of the generator to improve the accuracy of delineation.The experimental data included CT images from 130 cases of nasopharyngeal carcinoma.The images were preprocessed before model training.In addition,three models of UNet,GAN,and GAN with an attention mechanism were compared,and Dice similarity coefficient,Hausdorff distance,accuracy,Matthews correlation coefficient,Jaccard distance were employed to evaluate network performance.Results Compared with UNet,GAN and GAN with the attention mechanism,the average Dice similarity coefficient of 2D‐PE‐GAN network segmentation of CTV was increased by 26%,4%and 2%.The average Dice similarity coefficient of GTV segmentation was increased by 21%,4%,2%,respectively.Compared with the GAN network with the attention mechanism,the parameters and time of 2D‐PE‐GAN were reduced by 0.16%and 18%,respectively.Conclusions Compared with the above three networks,2D‐PE‐GAN network can increase the segmentation accuracy of nasopharyngeal carcinoma target area delineation.At the same time,compared with the attention mechanism with similar reasons,2D‐PE‐GAN network can reduce the occupation of computing resources when the segmentation accuracy is not much different.