ABSTRACT

This study aimed to develop an automated delineation method of nasopharynx gross tumor volume (GTVnx) for nasopharyngeal carcinoma (NPC) in computed tomography (CT) image for radiotherapy applications. Inspired by ResNet and SENet’s strong ability to extract image features, we proposed a modified version of the 3D U-Net model with Res-blocks and SE-block for delineation of GTVnx. Besides, an automatic pre-processing method was proposed to crop the 3D region of interest (ROI) of GTVnx. Radiotherapy simulation CT images and corresponding manually delineated target of 205 NPC patients diagnosed with stage T1-T4 were used as datasets for training. Automated delineation models were generated based on CT combining contrast-enhanced CT (CE-CT) and CT alone, respectively. We compared the automatic delineation results against the manual delineated contours by radiation oncologists with 5-fold cross-validation to evaluate the performance of the proposed model. We also compared with the framework using 3D CNN and 2D DDNN, respectively. Besides, the model generated by one medical group was assessed against the other two separate medical groups. Precision (PR), Sensitivity (SE), Dice Similarity Coefficient (DSC), Average Symmetric Surface Distance (ASSD), and 95% Hausdorff Distance (HD95) are calculated for quantitative evaluation. Experimental results show that the proposed method outperforms other automatic methods on the CT images. Automated delineation models based on CT combining CE-CT is superior to that based on CT alone. The presented method could be useful and robust for the 3D delineation of GTVnx for NPC in CT images during the planning of radiotherapy.

摘要

这项研究的目的是在计算机X射线断层扫描（CT）图像中开发一种用于鼻咽癌（NPC）的鼻咽总肿瘤体积（GTVnx）的自动描绘方法，以进行放射治疗。受ResNet和SENet强大的图像特征提取能力的启发，我们提出了带有Res-blocks和SE-block的3D U-Net模型的改进版本，以描绘GTVnx。此外，提出了一种自动预处理方法来裁剪GTVnx的3D感兴趣区域（ROI）。将205例经诊断为T1-T4期的NPC患者的放疗模拟CT图像和相应的人工划定目标用作训练数据集。分别基于对比增强CT（CE-CT）和单独CT的CT生成自动轮廓模型。我们比较了放射肿瘤科医生的自动划定结果与手动划定的轮廓，并进行了5倍交叉验证，以评估所提出模型的性能。我们还分别与使用3D CNN和2D DDNN的框架进行了比较。此外，一个医疗小组生成的模型针对其他两个单独的医疗小组进行了评估。计算精度（PR），灵敏度（SE），骰子相似系数（DSC），平均对称表面距离（ASSD）和95％Hausdorff距离（HD95）以进行定量评估。实验结果表明，该方法在CT图像上优于其他自动方法。基于CT结合CE-CT的自动描绘模型优于仅基于CT的模型。