Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common cancers of the nasopharynx. A structural analysis of NPC can provide vital insights into methods of treatment. However, manually marking the boundaries of NPC in images is tedious, time-consuming, and prone to error. It has become necessary to use computer-based automatic segmentation algorithms to accurately locate NPC. However, this remains a challenging task owing to the high variation (in shape and size) in the structure of the nasopharynx across subjects. Moreover, the nasopharyngeal area is small, and this causes severe imbalance in the foreground and background categories. In this paper, we propose a 3D convolutional neural network with long-range skip connection and multi-scale feature pyramid (SFP) for the segmentation of images of NPC. Unlike the traditional skip connection in residual blocks, which only considers the feature transfer and feature fusion between the same convolutional layer, long-range skip connection with original features from the first convolution in our network is passed to each down-sampling stage using element-wise sum to effectively increase reuse of low-level features and to solve the problems of gradient disappearance and explosion. The multi-scale feature pyramid with a varying atrous rate adapts to images of different sizes to learn multi-scale features, and hierarchical contextual information regarding NPC. To accelerate the convergence of our network, we use deep supervision to generate three auxiliary segmentation maps and merge the weighted loss into the objective function. And we fuse these auxiliary segmentation maps to refine the final segmentation result. In our experiments, the proposed network was trained and tested on 3D magnetic resonance imaging (MRI) images of 120 clinical patients using 5-fold cross-validation. The average dice similarity coefficient (DSC) and average symmetric surface distance (ASSD), used as evaluation metric, were 0.737 and 1.214 mm, respectively. This shows that in terms of results, our method is superior to five state-of-the-art networks and equivalent to the judgment of an experienced physician.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems
Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention, pp 424–432
Dan CC, Giusti A, Gambardella LM (2012) Schmidhuber: deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inf Process Syst 25:2852–2860
Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng PA (2016) 3d deeply supervised network for automatic liver segmentation from ct volumes, pp 149–157
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
Geneva, (2012) Union for international cancer control. PLoS ONE, 7(8):e42935
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification, pp 1026–1034
Huang G, Liu Z, Maaten LVD, Weinberger KQ (2016) Densely connected convolutional networks
Huang KW, Zhao ZY, Gong Q, Zha J, Chen L, Yang R (2015) Nasopharyngeal carcinoma segmentation via hmrf-em with maximum entropy. In: Engineering in medicine and biology society, p 2968
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift, pp 448–456
Kayalibay B, Jensen G, Smagt PVD (2017) Cnn-based segmentation of medical imaging data. arXiv:1701.03056
Lin L, Dou Q, Jin YM, Zhou GQ, Tang YQ, Chen WL, Su BA, Liu F, Tao CJ, Jiang N et al (2019) Deep learning for automated contouring of primary tumor volumes by mri for nasopharyngeal carcinoma. Radiology 291(3):677–686
Long J, Shelhamer E, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651
Lozano R (2012) Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. The Lancet 380:2095–2128. https://doi.org/10.1016/S0140-6736(12)61728-0
Ma Z, Zhou S, Wu X, Zhang H, Yan W, Sun S, Zhou J (2019) Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning. Phys Med Biol 64(2):025005. https://doi.org/10.1088/1361-6560/aaf5da
Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Fourth international conference on 3d Vision, pp 565–571
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241
Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging 24:588–599
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions
Zhao L, Lu Z, Jiang J, Zhou Y, Wu Y, Feng Q (2019) Automatic nasopharyngeal carcinoma segmentation using fully convolutional networks with auxiliary paths on dual-modality pet-ct images. J Digit Imaging 32(3):462–470
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61602066) and the Scientific Research Foundation (KYTZ201608) of CUIT and the major Project of Education Department in Sichuan (17ZA0063 and 2017JQ0030) and partially supported by the Sichuan international science and technology cooperation and exchange research program (2016HH0018), and the Project of Sichuan Outstanding Young Scientific and Technological Talents (19JCQN0003). This study was funded by 61602066, 17ZA0063, KYTZ201608, 2017JQ0030, 2016HH0018, and 19JCQN0003.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Human and animal rights
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.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Guo, F., Shi, C., Li, X. et al. Image segmentation of nasopharyngeal carcinoma using 3D CNN with long-range skip connection and multi-scale feature pyramid. Soft Comput 24, 12671–12680 (2020). https://doi.org/10.1007/s00500-020-04708-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-04708-y