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MSLPNet: multi-scale location perception network for dental panoramic X-ray image segmentation

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Abstract

Tooth segmentation, as one of the key techniques of medical image segmentation, can be widely applied to various medical applications, e.g., orthodontic treatment, corpse identification, dental training systems, dental disease diagnosis, etc. Although there have been many studies on tooth segmentation, few tooth segmentation studies have focused on enhancing tooth segmentation with fuzzy root boundaries which is a difficult but essential task in dentistry to determine the root resorption and the tooth brace. The existing methods for tooth segmentation usually exploited the contrast enhancement to sharpen the boundaries of teeth, while the final segmentation results heavily depended on the subsequent processing steps of the method. To address the issue of fuzzy boundaries, this paper proposes a novel multi-scale location perception network to segment teeth from panoramic X-ray images. The core of the proposed method stems from three aspects: (1) multi-scale structural similarity loss conducts accurate prediction with clear boundary from patch-level scale; (2) location perception module locates each tooth pixel in the image from the perspective of global-level scale; (3) aggregation module reduces the semantic gap between multi-scale feature branches. Our proposed method was tested on a dataset containing 1500 dental panoramic X-ray images and the Dice score of our method is 93.01%, which outperforms the state-of-the-art approaches. Besides, our proposed method has better boundary quality and the Pratt (1978)’s figure of merit used for boundary quality evaluation reaches 76.56%.

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References

  1. Kudo K, Okada Y (2020) Development of training system for dental treatment using webar and leap motion controller. In: Conference on complex, intelligent, and software intensive systems. Springer, Berlin, pp 579–587

  2. Liu L, Zhou R (2020) Simulation training for ceramic crown preparation in the dental setting using a virtual educational system. Eur J Dent Educ 24(2):199–206

    Article  MathSciNet  Google Scholar 

  3. Marroquin TY, Karkhanis S (2020) Overcoming population differences for dental age estimation in adults through pulp/tooth volume calculations: a pilot study. Aust J Forensic Sci 52(5):500–507

    Article  Google Scholar 

  4. Sehrawat JS, Singh M (2020) Dental age estimation of ajnala skeletal remains: a forensic odontological study. Bull Int Assoc Paleodontol 14(1):40–52

    Google Scholar 

  5. Lin PL, Huang PY (2012) An automatic lesion detection method for dental X-ray images by segmentation using variational level set. In: 2012 International conference on machine learning and cybernetics, vol 5. IEEE, pp 1821–1825

  6. Radhiyah A, Harsono T (2016) Comparison study of gaussian and histogram equalization filter on dental radiograph segmentation for labelling dental radiograph. In: 2016 International conference on knowledge creation and intelligent computing. IEEE, pp 253–258

  7. Rad AE, Rahim MSM (2013) Digital dental X-ray image segmentation and feature extraction. Indones J Electr Eng 11(6):3109–3114

    Google Scholar 

  8. Li H, Sun G (2012) Watershed algorithm based on morphology for dental X-ray images segmentation. In: 2012 IEEE 11th International conference on signal processing, vol 2. IEEE, pp 877–880

  9. Wirtz A, Mirashi SG (2018) Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 712–719

  10. Koch TL, Perslev M (2019) Accurate segmentation of dental panoramic radiographs with U-NETS. In: 2019 IEEE 16th International symposium on biomedical imaging. IEEE, pp 15–19

  11. Lim DH (2006) Robust edge detection in noisy images. Comput Stat Data Anal 50(3):803–812

    Article  MathSciNet  Google Scholar 

  12. Alsmadi MK (2018) A hybrid fuzzy c-means and neutrosophic for jaw lesions segmentation. Ain Shams Eng J 9(4):697–706

    Article  Google Scholar 

  13. Son LH, Tuan TM et al (2016) A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 46:380–393

    Article  Google Scholar 

  14. Ali RB, Ejbali R (2015) Gpu-based segmentation of dental X-ray images using active contours without edges. In: 2015 15th International conference on intelligent systems design and applications. IEEE, pp 505–510

  15. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  16. Li S, Fevens T (2007) Semi-automatic computer aided lesion detection in dental X-rays using variational level set. Pattern Recognit 40(10):2861–2873

    Article  Google Scholar 

  17. Li S, Fevens T (2006) An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments. Comput Med Imaging Graph 30(2):65–74

    Article  Google Scholar 

  18. Al-Janabi S, Alkaim AF (2020) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569

    Article  Google Scholar 

  19. Verhaeghe H, Nijssen S (2020) Learning optimal decision trees using constraint programming. Constraints 25:1–25

    Article  MathSciNet  Google Scholar 

  20. Speiser JL, Miller ME (2019) A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl 134:93–101

    Article  Google Scholar 

  21. Nomir O, Abdel-Mottaleb M (2008) Hierarchical contour matching for dental X-ray radiographs. Pattern Recognit 41(1):130–138

    Article  Google Scholar 

  22. Said EH, Nassar DEM (2006) Teeth segmentation in digitized dental X-ray films using mathematical morphology. IEEE Trans Inf Forensics Secur 1(2):178–189

    Article  Google Scholar 

  23. Mao J, Wang K (2018) Grabcut algorithm for dental x-ray images based on full threshold segmentation. IET Image Process 12(12):2330–2335

    Article  Google Scholar 

  24. Indraswari R, Arifin AZ (2015) Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding. In: 2015 International conference on information and communication technology and systems. IEEE, pp 49–54

  25. Ahmad NS, Zaki ZM (2014) Region of adaptive threshold segmentation between mean, median and otsu threshold for dental age assessment. In: 2014 International conference on computer, communications, and control technology. IEEE, pp 353–356

  26. Jain AK, Chen H (2004) Matching of dental X-ray images for human identification. Pattern Recognit 37(7):1519–1532

    Article  Google Scholar 

  27. Jiang F, Grigorev A (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257–1265

    Article  Google Scholar 

  28. Gómez O, Mesejo P (2019) Deep architectures for high-resolution multi-organ chest X-ray image segmentation. Neural Comput Appl 32:1–15

    Google Scholar 

  29. Zhang Y, Wang S (2020) CT image classification based on convolutional neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04933-4

    Article  Google Scholar 

  30. Jiang Y, Chen W (2020) 3D neuron microscopy image segmentation via the Ray-Shooting model and a DC-BLSTM network. IEEE Trans Med Imaging 40(1):26–37

    Article  MathSciNet  Google Scholar 

  31. Yang B, Chen W (2020) Neuron image segmentation via learning deep features and enhancing weak neuronal structures. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2020.3017540

    Article  Google Scholar 

  32. Ding Z, Mei G, Cuomo S, Li Y, Xu N (2020) Comparison of estimating missing values in iot time series data using different interpolation algorithms. Int J Parallel Program 48(3):534–548

    Article  Google Scholar 

  33. Sánchez-Morales A, Sancho-Gómez JL, Martínez-García JA, Figueiras-Vidal AR (2020) Improving deep learning performance with missing values via deletion and compensation. Neural Comput Appl 32(17):13233–13244

    Article  Google Scholar 

  34. Zhao H, Shi J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 2881–2890

  35. Sun K, Zhao Ya (2019) High-resolution representations for labeling pixels and regions. arXiv preprint arXiv:1904.04514

  36. He J, Deng Z (2019) Adaptive pyramid context network for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7519–7528

  37. Yang M, Yu K (2018) Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 3684–3692

  38. Qin X, Zhang Z (2019) Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 7479–7489

  39. Fu J, Liu J (2019) Adaptive context network for scene parsing. In: Proceedings of the IEEE international conference on computer vision, pp 6748–6757

  40. Yu C, Wang J (2018) Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision, pp 325–341

  41. Liu S, Qi L (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 8759–8768

  42. Jader G, Fontineli J (2018) Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI conference on graphics, patterns and images. IEEE, pp 400–407

  43. He K, Gkioxari G (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

  44. Ronneberger O, Fischer P (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 234–241

  45. Dong L, Zhang H (2020) Crowd counting by using multi-level density-based spatial information: a multi-scale CNN framework. Inf Sci 528:79–91

    Article  MathSciNet  Google Scholar 

  46. Lu X, Yao H (2019) Action recognition with multi-scale trajectory-pooled 3D convolutional descriptors. Multimed Tools Appl 78(1):507–523

    Article  Google Scholar 

  47. Chen JW, Wang R (2020) A convolutional neural network with parallel multi-scale spatial pooling to detect temporal changes in SAR images. Remote Sens 12(10):1619

    Article  Google Scholar 

  48. Vaccaro F, Bertini M (2020) Image retrieval using multi-scale CNN features pooling. In: Proceedings of the 2020 international conference on multimedia retrieval, pp 311–315

  49. Wang Z, Simoncelli E (2003) Multi-scale structural similarity for image quality assessment. In: The thirty-seventh Asilomar conference on signals, systems and computers, 2003, vol 2. IEEE, pp 1398–1402

  50. Milletari F, Navab N (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision. IEEE, pp 565–571

  51. De Boer PT, Kroese DP (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19–67

    Article  MathSciNet  Google Scholar 

  52. Silva G, Oliveira L (2018) Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Syst Appl 107:15–31

    Article  Google Scholar 

  53. Khoshdeli M, Winkelmaier G (2018) Fusion of encoder–decoder deep networks improves delineation of multiple nuclear phenotypes. BMC Bioinform 19(1):294

    Article  Google Scholar 

  54. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. CoRR arXiv:1412.6980

  55. Badrinarayanan V, Kendall A (2017) Segnet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans Pattern Anal Mach Intell 39(12): 2481–2495

    Article  Google Scholar 

  56. Li H, Xiong P (2019) Dfanet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 9522–9531

  57. Wang G, Liu X (2020) A noise-robust framework for automatic segmentation of covid-19 pneumonia lesions from CT images. IEEE Trans Med Imaging 39(8):2653–2663

    Article  Google Scholar 

  58. Zhao Y, Li P, Gao C (2020) TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network. Knowl Based Syst 206:106338

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61571071, 61906025), Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2018jcyjAX0227, cstc2020jcyj-msxmX0835), the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant (No. KJQN201900607, KJQN202000647).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qiaoyi Chen, Yue Zhao, Pengcheng Li, Lingming Zhang & Chenqiang Gao

  2. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qiaoyi Chen, Yue Zhao, Pengcheng Li, Lingming Zhang & Chenqiang Gao

  3. Orthodontic Department, Stomatological Hospital of Chongqing Medical University, Chongqing, 400065, China

    Yang Liu & Chongshi Yang

  4. NTT Service Evolution Laboratories, Yokosuka, 239-0087, Japan

    Yongqing Sun

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  1. Qiaoyi Chen
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  2. Yue Zhao
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Correspondence to Yue Zhao.

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Chen, Q., Zhao, Y., Liu, Y. et al. MSLPNet: multi-scale location perception network for dental panoramic X-ray image segmentation. Neural Comput & Applic 33, 10277–10291 (2021). https://doi.org/10.1007/s00521-021-05790-5

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