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Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

  • Jinlian Ma
  • Fa Wu
  • Tian’an Jiang
  • Qiyu Zhao
  • Dexing KongEmail author
Original Article

Abstract

Purpose

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images.

Methods

Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset.

Results

The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as \(0.8683 \pm 0.0056\), \(0.9224 \pm 0.0027\), \(0.915 \pm 0.0077\), \(0.0669 \pm 0.0032\), \(0.6228 \pm 0.1414\) on overall folds, respectively.

Conclusion

Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

Keywords

Thyroid nodule Ultrasound image Convolutional neural network Segmentation 

Notes

Acknowledgements

This work was supported in part by the National 372 Natural Science Foundation of China (Grant No. 91630311), the Fun-373 damental Research Funds for the Central Universities (Grant No. 374 2017XZZX007-02). The authors would like to thank Dr. Deepika Koundal, University Institute of Engineering and Technology, Panjab University, Chandigarh, India, for kindly providing their code.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study

References

  1. 1.
    Bushberg JT, Boone JM (2011) The essential physics of medical imaging. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  2. 2.
    Chang CY, Huang HC, Chen SJ (2010) Automatic thyroid nodule segmentation and component analysis in ultrasound images. Biomed Eng Appl Basis Commun 22(02):81–89CrossRefGoogle Scholar
  3. 3.
    Chang CY, Lei YF, Tseng CH, Shih SR (2010) Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans Biomed Eng 57(6):1348–1357CrossRefPubMedGoogle Scholar
  4. 4.
    Chen YW, Lin CJ (2006) Combining SVMS with various feature selection strategies. In: Guyon I, Nikravesh M, Gunn S, Zadeh LA (eds) Feature extraction. Studies in Fuzziness and Soft Computing, vol 42. Springer, Berlin, pp 315–324Google Scholar
  5. 5.
    Chikui T, Okamura K, Tokumori K, Nakamura S, Shimizu M, Koga M, Yoshiura K (2006) Quantitative analyses of sonographic images of the parotid gland in patients with sjögrens syndrome. Ultrasound Med Biol 32(5):617–622CrossRefPubMedGoogle Scholar
  6. 6.
    Ciresan D, Giusti A, Gambardella L M, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems, pp 2843–2851Google Scholar
  7. 7.
    Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3642–3649Google Scholar
  8. 8.
    Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Medical image computing and computer-assisted intervention—MICCAI 2013. Springer, pp 411–418Google Scholar
  9. 9.
    Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202CrossRefPubMedGoogle Scholar
  10. 10.
    He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. arXiv preprint arXiv:1502.01852
  11. 11.
    Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500(7461):168–174CrossRefPubMedGoogle Scholar
  12. 12.
    Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580
  13. 13.
    Iakovidis DK, Savelonas MA, Karkanis SA, Maroulis DE (2007) A genetically optimized level set approach to segmentation of thyroid ultrasound images. Appl Intell 27(3):193–203CrossRefGoogle Scholar
  14. 14.
    Koundal D, Gupta S, Singh S (2016) Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Appl Soft Comput 40:86–97CrossRefGoogle Scholar
  15. 15.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  16. 16.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  17. 17.
    Li BN, Chui CK, Chang S, Ong SH (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 41(1):1–10CrossRefPubMedGoogle Scholar
  18. 18.
    Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 19(12):3243–3254Google Scholar
  19. 19.
    National Cancer Institute (2016) Thyroid cancer information. http://www.cancer.gov/cancertopics/types/thyroid
  20. 20.
    MacKay DJ (1995) Probable networks and plausible predictionsa—a review of practical Bayesian methods for supervised neural networks. Netw Comput Neural Syst 6(3):469–505CrossRefGoogle Scholar
  21. 21.
    Papini E, Guglielmi R, Bianchini A, Crescenzi A, Taccogna S, Nardi F, Panunzi C, Rinaldi R, Toscano V, Pacella CM (2002) Risk of malignancy in nonpalpable thyroid nodules: predictive value of ultrasound and color-Doppler features. J Clin Endocrinol Metab 87(5):1941–1946CrossRefPubMedGoogle Scholar
  22. 22.
    Ravishankar H, Prabhu SM, Vaidya V, Singhal N (2016) Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. In: IEEE international symposium on biomedical imagingGoogle Scholar
  23. 23.
    Savelonas MA, Iakovidis DK, Legakis I, Maroulis D (2009) Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images. IEEE Trans Inf Technol Biomed 13(4):519–527CrossRefPubMedGoogle Scholar
  24. 24.
    Selvathi D, Sharnitha V (2011) Thyroid classification and segmentation in ultrasound images using machine learning algorithms. In: 2011 international conference on Signal processing, communication, computing and networking technologies (ICSCCN), pp 836–841. IEEEGoogle Scholar
  25. 25.
    Turaga SC, Murray JF, Jain V, Roth F, Helmstaedter M, Briggman K, Denk W, Seung HS (2010) Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput 22(2):511–538CrossRefPubMedGoogle Scholar
  26. 26.
    Wan L, Zeiler M, Zhang S, Cun YL, Fergus R (2013) Regularization of neural networks using dropconnect. In: Proceedings of the 30th international conference on machine learning (ICML-13), pp 1058–1066Google Scholar
  27. 27.
    Wang L, Shi F, Gao Y, Li G, Gilmore JH, Lin W, Shen D (2014) Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain mr image segmentation. NeuroImage 89:152–164CrossRefPubMedGoogle Scholar
  28. 28.
    Wu F, Hu P, Kong D (2015) Flip-rotate-pooling convolution and split dropout on convolution neural networks for image classification. arXiv preprint arXiv:1507.08754
  29. 29.
    Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–224CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.State Key Lab of CAD&CG, College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.School of Mathematical SciencesZhejiang UniversityHangzhouChina
  3. 3.Department of UltrasoundFirst Affiliated Hospital, Zhejiang UniversityHangzhouChina

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