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Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning

  • Shujun Liang
  • Fan Tang
  • Xia Huang
  • Kaifan Yang
  • Tao Zhong
  • Runyue Hu
  • Shangqing Liu
  • Xinrui Yuan
  • Yu Zhang
Head and Neck
  • 62 Downloads

Abstract

Objective

Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs.

Methods

The ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samples t tests and analysis of variance were employed for statistical analysis.

Results

ODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p = 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients.

Conclusion

The ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC.

Key Points

• A fully automated deep-learning method (ODS net) is developed to detect and segment OARs in clinical CT images.

• This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning.

This deep-learning-based framework delineating a single image requires approximately 30 s, which is suitable for clinical workflows.

Keywords

Image processing Tomography, x-ray computed Head and neck neoplasms Organs at risk Radiotherapy 

Abbreviations

CNN

Convolutional neural network

FCN

Fully convolutional neural network

GPU

Graphics processing unit

NPC

Nasopharyngeal carcinoma

OARs

Organs at risk

ODS net

Organs-at-risk detection and segmentation network

Notes

Acknowledgements

The author(s) would like to thank the reviewers for their fruitful comments.

Funding

This study has received funding by the National Natural Science Foundation of China under Grant No. 61671230 and No.31271067, the Science and Technology Program of Guangdong Province under Grant No. 2017A020211012, the Guangdong Provincial Key Laboratory of Medical Image Processing under Grant No.2014B030301042, and the Science and Technology Program of Guangzhou under Grant No. 201607010097.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Yu Zhang.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• experimental

• performed at one institution

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Shujun Liang
    • 1
  • Fan Tang
    • 1
    • 2
  • Xia Huang
    • 1
  • Kaifan Yang
    • 3
  • Tao Zhong
    • 1
  • Runyue Hu
    • 1
  • Shangqing Liu
    • 1
  • Xinrui Yuan
    • 1
  • Yu Zhang
    • 1
  1. 1.Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  2. 2.Department of Radiation Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouChina
  3. 3.Department of Medical Imaging Center, Nanfang HospitalSouthern Medical UniversityGuangzhouChina

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