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Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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.

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Funding

This work was funded in part by grants from the National Key R&D Program of China (Grant No. 2017YFB1302900), Natural Science Foundation of Shandong Province (ZR2020MH227), and SJTU Translational Medicine Cross Research Fund (YG2019ZDA26).

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Yuxiao Qi, Jieyu Li, Huai Chen & Lisheng Wang

  2. Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People’s Republic of China

    Yujie Guo, Yong Yin & Guanzhong Gong

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  1. Yuxiao Qi
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  2. Jieyu Li
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  3. Huai Chen
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  6. Guanzhong Gong
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  7. Lisheng Wang
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Correspondence to Jieyu Li or Guanzhong Gong.

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This retrospective study was conducted following ethical approval from the Institutional Review Board at the Hospital of the Shandong Cancer Hospital and Institute. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Qi, Y., Li, J., Chen, H. et al. Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images. Int J CARS 16, 871–882 (2021). https://doi.org/10.1007/s11548-021-02351-y

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