基于不同感受野的鼻咽癌靶区和危及器官自动分割网络研究

目的 研究基于不同感受野的鼻咽癌靶区和危及器官自动分割网络。方法 收集 100例鼻咽癌患者放疗数据,包含患者CT图像和医生勾画的靶区(GTV)和危及器官。随机选取 90例数据作为训练集,另 10例作为验证集。首先对图像进行中心裁剪、随机垂直翻转和旋转(-30°~30°)数据增强方式,输入至本文提出的MA_net网络进行训练,通过网络参数、浮点运算数、运行内存和Dice系数评估该网络性能;最后将其与当前主流的分割网络DeeplabV3+、PSP_net、UNet++、U_Net比较。结果 当输入图像为240×240时,MA_net网络参数分别为4个网络的23.20%、20.10%、25.55%和27.11%;其浮点运算数分别为4个网络的50.02%、19.86%、6.37%和13.44%;其运行内存分别为4个网络的40.63%、23.60%、11.58%和14.99%;GTV的分割结果显示MA_net的Dice系数比4个网络分别高出1.16%、2.28%、1.27%和3.59%;GTV与危及器官的分割结果显示MA_net的平均Dice系数比4个网络分别高出0.16%、1.37%、0.30%和0.97%。结论 相比于上述4个网络,MA_net参数少、运算浮点数低、运行内存小且Dice系数有所提升。

Different receptive fields-based automatic segmentation network for gross target volume and organs at risk of patients with nasopharyngeal carcinoma

Objective To establish an automatic segmentation network based on different receptive fields for gross target volume (GTV) and organs at risk in patients with nasopharyngeal carcinoma. Methods Radiotherapy data of 100 cases of nasopharyngeal carcinoma including CT images and GTV and organs at risk delineated by the physicians were collected. Ninety plans were randomly selected as the training dataset, and the other 10 plans as the validation dataset. Firstly, the images were subject to three data augmentation methods including center cropping, vertical flipping and rotation (-30°to 30°), and then input into MA_net networks proposed in this study for training. The model performance of networks was assessed by the number of network parameters (NP), floating-point number (FPN), the running memory (RM) and Dice index (DI), and eventually compared with DeeplabV3+, PSP_net, UNet++ and U_Net networks. Results When the input image was in the size of 240×240, MA_net had a NP of 23.20%, 20.10%, 25.55% and 27.11% of these 4 networks, 50.02%, 19.86%, 6.37% and 13.44% for the FPN, 40.63%, 23.60%, 11.58% and 14.99% for the RM, respectively. For the DI of GTV, MA_net was 1.16%, 2.28%, 1.27% and 3.59% higher than these 4 networks. For the average DI of GTV and OAR, MA_net was 0.16%, 1.37%, 0.30% and 0.97% higher than these 4 networks. Conclusion Compared with those four networks, the proposed MA_net network has slightly higher Dice index with fewer parameters, lower FPN and smaller RM.