Spandidos Publications Logo
  • About
    • About Spandidos
    • Aims and Scopes
    • Abstracting and Indexing
    • Editorial Policies
    • Reprints and Permissions
    • Job Opportunities
    • Terms and Conditions
    • Contact
  • Journals
    • All Journals
    • Oncology Letters
      • Oncology Letters
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Oncology
      • International Journal of Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular and Clinical Oncology
      • Molecular and Clinical Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Experimental and Therapeutic Medicine
      • Experimental and Therapeutic Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Molecular Medicine
      • International Journal of Molecular Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Biomedical Reports
      • Biomedical Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Reports
      • Oncology Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular Medicine Reports
      • Molecular Medicine Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • World Academy of Sciences Journal
      • World Academy of Sciences Journal
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Functional Nutrition
      • International Journal of Functional Nutrition
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Epigenetics
      • International Journal of Epigenetics
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Medicine International
      • Medicine International
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
  • Articles
  • Information
    • Information for Authors
    • Information for Reviewers
    • Information for Librarians
    • Information for Advertisers
    • Conferences
  • Language Editing
Spandidos Publications Logo
  • About
    • About Spandidos
    • Aims and Scopes
    • Abstracting and Indexing
    • Editorial Policies
    • Reprints and Permissions
    • Job Opportunities
    • Terms and Conditions
    • Contact
  • Journals
    • All Journals
    • Biomedical Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Experimental and Therapeutic Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Epigenetics
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Functional Nutrition
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Molecular Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Medicine International
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular and Clinical Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular Medicine Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Letters
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • World Academy of Sciences Journal
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
  • Articles
  • Information
    • For Authors
    • For Reviewers
    • For Librarians
    • For Advertisers
    • Conferences
  • Language Editing
Login Register Submit
  • This site uses cookies
  • You can change your cookie settings at any time by following the instructions in our Cookie Policy. To find out more, you may read our Privacy Policy.

    I agree
Search articles by DOI, keyword, author or affiliation
Search
Advanced Search
presentation
Experimental and Therapeutic Medicine
Join Editorial Board Propose a Special Issue
Print ISSN: 1792-0981 Online ISSN: 1792-1015
Journal Cover
September-2018 Volume 16 Issue 3

Full Size Image

Sign up for eToc alerts
Recommend to Library

Journals

International Journal of Molecular Medicine

International Journal of Molecular Medicine

International Journal of Molecular Medicine is an international journal devoted to molecular mechanisms of human disease.

International Journal of Oncology

International Journal of Oncology

International Journal of Oncology is an international journal devoted to oncology research and cancer treatment.

Molecular Medicine Reports

Molecular Medicine Reports

Covers molecular medicine topics such as pharmacology, pathology, genetics, neuroscience, infectious diseases, molecular cardiology, and molecular surgery.

Oncology Reports

Oncology Reports

Oncology Reports is an international journal devoted to fundamental and applied research in Oncology.

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine is an international journal devoted to laboratory and clinical medicine.

Oncology Letters

Oncology Letters

Oncology Letters is an international journal devoted to Experimental and Clinical Oncology.

Biomedical Reports

Biomedical Reports

Explores a wide range of biological and medical fields, including pharmacology, genetics, microbiology, neuroscience, and molecular cardiology.

Molecular and Clinical Oncology

Molecular and Clinical Oncology

International journal addressing all aspects of oncology research, from tumorigenesis and oncogenes to chemotherapy and metastasis.

World Academy of Sciences Journal

World Academy of Sciences Journal

Multidisciplinary open-access journal spanning biochemistry, genetics, neuroscience, environmental health, and synthetic biology.

International Journal of Functional Nutrition

International Journal of Functional Nutrition

Open-access journal combining biochemistry, pharmacology, immunology, and genetics to advance health through functional nutrition.

International Journal of Epigenetics

International Journal of Epigenetics

Publishes open-access research on using epigenetics to advance understanding and treatment of human disease.

Medicine International

Medicine International

An International Open Access Journal Devoted to General Medicine.

Journal Cover
September-2018 Volume 16 Issue 3

Full Size Image

Sign up for eToc alerts
Recommend to Library

  • Article
  • Citations
    • Cite This Article
    • Download Citation
    • Create Citation Alert
    • Remove Citation Alert
    • Cited By
  • Similar Articles
    • Related Articles (in Spandidos Publications)
    • Similar Articles (Google Scholar)
    • Similar Articles (PubMed)
  • Download PDF
  • Download XML
  • View XML
Article Open Access

Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut

  • Authors:
    • Zongqing Ma
    • Xi Wu
    • Qi Song
    • Yong Luo
    • Yan Wang
    • Jiliu Zhou
  • View Affiliations / Copyright

    Affiliations: College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, P.R. China, School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, P.R. China, CuraCloud Corp., Seattle, WA 98104, USA, Department of Head and Neck and Mammary Oncology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
    Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 2511-2521
    |
    Published online on: July 18, 2018
       https://doi.org/10.3892/etm.2018.6478
  • Expand metrics +
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )
Cited By (CrossRef): 0 citations Loading Articles...

This article is mentioned in:



Abstract

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in medical images is an import task for clinical applications, including radiotherapy. However, NPC features large variations in lesion size and shape, as well as inhomogeneous intensities within the tumor and similar intensity to that of nearby tissues, making its segmentation a challenging task. The present study proposes a novel automated NPC segmentation method in magnetic resonance (MR) images by combining a deep convolutional neural network (CNN) model and a 3‑dimensional (3D) graph cut‑based method in a two‑stage manner. First, a multi‑view deep CNN‑based segmentation method is performed. A voxel‑wise initial segmentation is generated by integrating the inferential classification information of three trained single‑view CNNs. Instead of directly using the CNN classification results to achieve a final segmentation, the proposed method uses a 3D graph cut‑based method to refine the initial segmentation. Specifically, the probability response map obtained using the multi‑view CNN method is utilized to calculate the region cost, which represents the likelihood of a voxel being assigned to the tumor or non‑tumor. Structure information in 3D from the original MR images is used to calculate the boundary cost, which measures the difference between the two voxels in the 3D neighborhood. The proposed method was evaluated on T1‑weighted images from 30 NPC patients using the leave‑one‑out method. The experimental results demonstrated that the proposed method is effective and accurate for NPC segmentation.
View Figures

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

View References

1 

Chang ET and Adami HO: The enigmatic epidemiology of nasopharyngeal carcinoma. Cancr Epidemiol Biomarkers Prev. 15:1765–1777. 2006. View Article : Google Scholar

2 

Lee FK, Yeung DK, King AD, Leung SF and Ahuja A: Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images. Int J Radiat Oncol Biol Phys. 61:608–620. 2005. View Article : Google Scholar : PubMed/NCBI

3 

Tatanun C, Ritthipravat P, Bhongmakapat T and Tuntiyatorn L: Automatic segmentation of nasopharyngeal carcinoma from CT images: Region growing based technique. Signal Processing Systems (ICSPS), 2010. 2nd International Conference on. 2010.(DOI: 10.1109/ICSPS.2010.5555663). View Article : Google Scholar

4 

Chanapai W, Bhongmakapat T, Tuntiyatorn L and Ritthipravat P: Nasopharyngeal carcinoma segmentation using a region growing technique. Int J Comput Assist Radiol Surg. 7:413–422. 2012. View Article : Google Scholar : PubMed/NCBI

5 

Huang KW, Zhao ZY, Gong Q, Zha J, Chen L and Yang R: Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy. Conf Proc IEEE Eng Med Biol Soc. 2968–2972. 2015.(DOI: 10.1109/EMBC.2015.7319015). PubMed/NCBI

6 

Fitton I, Cornelissen SA, Duppen JC, Steenbakkers RJ, Peeters ST, Hoebers FJ, Kaanders JH, Nowak PJ, Rasch CR and van Herk M: Semi-automatic delineation using weighted CT-MRI registered images for radiotherapy of nasopharyngeal cancer. Med Phys. 38:4662–4666. 2011. View Article : Google Scholar : PubMed/NCBI

7 

Zhou J, Chan KL, Xu P and Chong VFH: Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine. In Biomedical Imaging: Nano to Macro, 2006. The 3rd IEEE International Symposium on. 2006.(DOI: 10.1109/ISBI.2006.1625180).

8 

Zhou J, Lim TK, Chong V and Huang J: Segmentation and visualization of nasopharyngeal carcinoma using MRI. Comput Biol Med. 33:407–424. 2003. View Article : Google Scholar : PubMed/NCBI

9 

LeCun Y, Bottou L, Bengio Y and Haffner P: Gradient-based learning applied to document recognition. Proc IEEE. 86:2278–2324. 1998. View Article : Google Scholar

10 

Krizhevsky A, Sutskever I and Hinton GE: ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 1:1097–1105, 2012. 2012.

11 

Prasoon A, Petersen K, Igel C, Lauze F, Dam E and Nielsen M: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv. 16:246–253. 2013.PubMed/NCBI

12 

Roth HR, Farag A, Lu L, Turkbey EB and Summers RM: Deep convolutional networks for pancreas segmentation in CT imaging. SPIE Med Imag. 94131G. 2015.(DOI: 10.1117/12.2081420).

13 

Liskowski P and Krawiec K: Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging. 35:2369–2380. 2016. View Article : Google Scholar : PubMed/NCBI

14 

Zhang W, Li R, Deng H, Wang L, Lin W, Ji S and Shen D: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage. 108:214–224. 2015. View Article : Google Scholar : PubMed/NCBI

15 

Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ and Isgum I: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging. 35:1252–1261. 2016. View Article : Google Scholar : PubMed/NCBI

16 

Pereira S, Pinto A, Alves V and Silva CA: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 35:1240–1251. 2016. View Article : Google Scholar : PubMed/NCBI

17 

Zikic D, Ioannou Y, Brown M and Criminisi A: Segmentation of brain tumor tissues with convolutional neural networks. MICCAI Multi Brain Tumor Segment Challeng (BraTS). 2014:36–39. 2014.

18 

Havaei M, Ddavy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM and Larochelle H: Brain tumor segmentation with deep neural networks. Med Image Anal. 35:18–31. 2017. View Article : Google Scholar : PubMed/NCBI

19 

Ma ZQ, Wu X and Zhou JL: Automatic nasopharyngeal carcinoma segmentation in MR images with convolutional neural networks. 2017 Int Conference Front Adv Data. 147–150. 2017. View Article : Google Scholar

20 

Tustison NJ, Avants BB, Cook PA, Zheng Y, Eqan A, Yushkevich PA and Gee JC: N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging. 29:1310–1320. 2010. View Article : Google Scholar : PubMed/NCBI

21 

Nyúl LG, Udupa JK and Zhang X: New variants of a method of MRI scale standardization. IEEE Trans Med Imaging. 19:143–150. 2000. View Article : Google Scholar : PubMed/NCBI

22 

Hinton GE, Srivastava N, Krizhevsky A, Sutskever I and Salakhutdinov RR: Improving neural networks by preventing co-adaptation of feature detectors. Neural Evolution Comput. 2012.

23 

Jarrett K, Kavukcuogly K, Ranzato M and LeCun Y: What is the best multi-stage architecture for object recognition? IEEE. 1–2153. 2009.

24 

Boykov Y, Veksler O and Zabih R: Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell. 23:1222–1239. 2001. View Article : Google Scholar

25 

Boykov Y and Funka-Lea G: Graph cuts and efficient N-D image segmentation. Int J Comput Vis. 70:109–131. 2006. View Article : Google Scholar

26 

Grosgeorge D, Petitjean C, Dacher JN and Ruan S: Graph cut segmentation with a statistical shape model in cardiac MRI. Comput Vis Image Understand. 117:1027–1035. 2013. View Article : Google Scholar

27 

Martinez-Muñoz S, Ruiz-Fernandez D and Galiana-Merino JJ: Automatic abdominal aortic aneurysm segmentation in MR images. Expert Syst Applicat. 54:78–87. 2016. View Article : Google Scholar

28 

Mahapatra D and Buhmann JM: Prostate MRI segmentation using learned semantic knowledge and graph cuts. IEEE Trans Biomed Eng. 61:756–764. 2014. View Article : Google Scholar : PubMed/NCBI

29 

Tian Z, Liu L, Zhang Z and Fei B: Superpixel-based segmentation for 3D prostate MR images. IEEE Trans Med Imaging. 35:791–801. 2016. View Article : Google Scholar : PubMed/NCBI

30 

Song Q, Bai J, Han D, Bhatia S, Sun W, Rockey W, Bayouth JE, Buatti JM and Wu X: Optimal co-segmentation of tumor in PET-CT images with context information. IEEE Trans Med Imaging. 32:1685–1697. 2013. View Article : Google Scholar : PubMed/NCBI

31 

Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrell T: Caffe: Convolutional architecture for fast feature embedding. Proceed of the 22nd ACM Int Conferen Multimedia. ACM; pp. 675–678. 2014

32 

Glorot X and Bengio Y: Understanding the difficulty of training deep feedforward neural networks. in Proc Int Conf Artif Intell Stat. 2010:249–256. 2010.

33 

Sutskever I, Martens J, Dahl G and Hinton G: On the importance of initialization and momentum in deep learning. PMLR. 28:1139–1147. 2013.

Related Articles

  • Abstract
  • View
  • Download
  • Twitter
Copy and paste a formatted citation
Spandidos Publications style
Ma Z, Wu X, Song Q, Luo Y, Wang Y and Zhou J: Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut. Exp Ther Med 16: 2511-2521, 2018.
APA
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., & Zhou, J. (2018). Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut. Experimental and Therapeutic Medicine, 16, 2511-2521. https://doi.org/10.3892/etm.2018.6478
MLA
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., Zhou, J."Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut". Experimental and Therapeutic Medicine 16.3 (2018): 2511-2521.
Chicago
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., Zhou, J."Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut". Experimental and Therapeutic Medicine 16, no. 3 (2018): 2511-2521. https://doi.org/10.3892/etm.2018.6478
Copy and paste a formatted citation
x
Spandidos Publications style
Ma Z, Wu X, Song Q, Luo Y, Wang Y and Zhou J: Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut. Exp Ther Med 16: 2511-2521, 2018.
APA
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., & Zhou, J. (2018). Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut. Experimental and Therapeutic Medicine, 16, 2511-2521. https://doi.org/10.3892/etm.2018.6478
MLA
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., Zhou, J."Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut". Experimental and Therapeutic Medicine 16.3 (2018): 2511-2521.
Chicago
Ma, Z., Wu, X., Song, Q., Luo, Y., Wang, Y., Zhou, J."Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut". Experimental and Therapeutic Medicine 16, no. 3 (2018): 2511-2521. https://doi.org/10.3892/etm.2018.6478
Follow us
  • Twitter
  • LinkedIn
  • Facebook
About
  • Spandidos Publications
  • Careers
  • Cookie Policy
  • Privacy Policy
How can we help?
  • Help
  • Live Chat
  • Contact
  • Email to our Support Team