Purpose

Nasopharyngeal carcinoma (NPC) is a category of tumors with high incidence in head-and-neck (H&N) body region, and the diagnosis and treatment planning are usually conducted by radiologists manually, which is tedious, time-consuming and unrepeatable. In this paper, we integrated different stages of this process and proposed a computer-aided framework to realize automatic detection, tumor region and sub-region segmentation, and visualization of NPC, which are usually investigated separately in literatures.

Methods

Multi-modality images are utilized in the framework. Firstly, NPC is detected by a convolutional neural network (CNN) on computed tomography (CT) scans. Then, NPC area is segmented from magnetic resonance imaging (MRI) images by using a multi-modality MRI fusion network. Thirdly, NPC sub-regions with different metabolic activities are divided on CT images of the same patient via an adaptive threshold algorithm. Finally, 3D surface model of NPC is generated for observing its shape, size, and location in the head region. The proposed method is compared with other algorithms by evaluation on the volumes and shapes of detected NPCs.

Results

Experiments are conducted on CT images of 130 NPC patients and 102 subjects without NPC and MRI images of 149 NPC patients, among which 52 subjects are overlapped with both CT and MRI images. The reference for evaluation is generated by three experienced radiologists. The results demonstrated that our utilized models outperform other strategies with detection accuracy 0.882 and Dice similarity coefficient 0.719 for NPC segmentation. Sub-region division and the 3D visualized models show great acceptability in clinical usage.

Conclusion

The remarkable performance indicated the potential of our framework in alleviating workload of radiologist. Furthermore, the combined usage of multi-modality images is able to generate reliable segmentations of NPC area and sub-regions, which provide evidence to judge the heterogeneity among patients and guide the dose painting for radiation therapy.

中文翻译：

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基于多模态医学图像的鼻咽癌计算机辅助诊断和区域分割

目的

鼻咽癌（NPC）是在头颈部（H＆N）身体区域高发的一类肿瘤，诊断和治疗计划通常由放射科医生手动进行，这很繁琐，耗时且不可重复。在本文中，我们集成了该过程的不同阶段，并提出了一个计算机辅助框架来实现自动检测，肿瘤区域和亚区域分割以及NPC的可视化，这些通常在文献中分别进行研究。

方法

框架中使用了多模式图像。首先，通过卷积神经网络（CNN）在计算机断层扫描（CT）扫描中检测NPC。然后，通过使用多模态MRI融合网络从磁共振成像（MRI）图像中分割出NPC区域。第三，通过自适应阈值算法在同一患者的CT图像上划分代谢活动不同的NPC子区域。最后，生成NPC的3D表面模型以观察其形状，大小和在头部区域中的位置。通过对检测到的NPC的体积和形状进行评估，将该方法与其他算法进行了比较。

结果

对130名NPC患者和102名无NPC患者的CT图像和149名NPC患者的MRI图像进行了实验，其中52名受试者均与CT和MRI图像重叠。评估参考由三位经验丰富的放射科医生提供。结果表明，对于NPC分割，我们使用的模型优于其他策略，其检测精度为0.882，Dice相似系数为0.719。分区划分和3D可视化模型在临床使用中显示出很高的可接受性。

结论

出色的性能表明我们的框架在减轻放射科医生工作量方面具有潜力。此外，多模态图像的组合使用能够生成NPC区域和子区域的可靠分割，从而提供证据来判断患者之间的异质性并指导放射治疗的剂量绘制。