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PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier

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Abstract

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by general dentists relying only on their visual inspection using dental x-rays. In many cases, dental caries is hard to identify in x-rays and can be misinterpreted as shadows due to the low image quality. In this research study, we propose an automatic diagnosis system to detect dental caries in Panoramic images, which benefits from various deep pretrained models through transfer learning to extract relevant features and uses a capsule network to draw prediction results. Using a dataset of 470 Panoramic images, our model achieved an accuracy of 86.05% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays are taken into account. Among carious samples, our model acquired recall scores of 69.44% and 90.52% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a larger dataset. Considering the novelty of current study as using Panoramic images, following work is a step towards developing a fully automated system to assist domain experts.

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References

  1. Abdel-Mottaleb M, Nomir O, Nassar DE, Fahmy G, Ammar HH (2003) Challenges of developing an automated dental identification system. In: 2003 46th midwest symposium on circuits and systems, vol 1. IEEE, pp 411–414

  2. Akarslan Z, Akdevelioglu M, Gungor K, Erten H (2008) A comparison of the diagnostic accuracy of bitewing, periapical, unfiltered and filtered digital panoramic images for approximal caries detection in posterior teeth. Dentomaxillofacial Radiology 37(8):458–463

    Article  Google Scholar 

  3. Akkaya N, Kansu O, Kansu H, Cagirankaya L, Arslan U (2006) Comparing the accuracy of panoramic and intraoral radiography in the diagnosis of proximal caries. Dentomaxillofacial Radiology 35(3):170–174

    Article  Google Scholar 

  4. Al-Sherif N, Guo G, Ammar HH (2012) A new approach to teeth segmentation. In: 2012 IEEE International Symposium on Multimedia. IEEE, pp 145–148

  5. Amrollahi P, Shah B, Seifi A, Tayebi L (2016) Recent advancements in regenerative dentistry: a review. Mater Sci Eng C 69:1383–1390

    Article  Google Scholar 

  6. Beltrán-Aguilar ED, Barker LK, Canto MT, Dye BA, Gooch BF, Griffin SO, Hyman J, Jaramillo F, Kingman A, Nowjack-Raymer R, et al. (2005) Surveillance for dental caries, dental sealants, tooth retention, edentulism, and enamel fluorosis; united states, 1988-1994 and 1999–2002

  7. Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, Krejci I, Markram H (2019) Caries detection with near-infrared transillumination using deep learning. J Dental Res 98(11):1227–1233

    Article  Google Scholar 

  8. Choi J, Eun H, Kim C (2016) Boosting proximal dental caries detection via combination of variational methods and convolutional neural network. Journal of Signal Processing Systems 90(1):87–97

    Article  Google Scholar 

  9. Fejerskov O, Kidd E (2009) Dental caries: the disease and its clinical management. Wiley

  10. Flint DJ, Paunovich E, Moore WS, Wofford DT, Hermesch CB (1998) A diagnostic comparison of panoramic and intraoral radiographs. Oral Surgery Oral Med Oral Pathol Oral Radiol Endodontol 85(6):731–735

    Article  Google Scholar 

  11. Fried D (2020) Detecting dental decay with infrared light. Optics Photonics News 31(5):48–53

    Article  Google Scholar 

  12. Frosst N, Sabour S, Hinton G (2018)

  13. Goldberg DE (2006) Genetic algorithms. Pearson Education, India

    Google Scholar 

  14. Haghanifar A, Amirkhani A, Mosavi MR (2018) Dental caries degree detection based on fuzzy cognitive maps and genetic algorithm

  15. Haghanifar A, Majdabadi MM, Ko S-B (2020) Automated teeth extraction from dental panoramic x-ray images using genetic algorithm. In: 2020 IEEE international symposium on circuits and systems (ISCAS). IEEE, pp 1–5

  16. Iesmantas T, Alzbutas R (2018) Convolutional capsule network for classification of breast cancer histology images. In: International conference image analysis and recognition. Springer, pp 853–860

  17. Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L (2018) Deep instance segmentation of teeth in panoramic x-ray images. In: 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 400–407

  18. Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, Muhammad M, Khurram SA (2020) Automated feature detection in dental periapical radiographs by using deep learning. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

  19. Laishram A, Thongam K (2020) Detection and classification of dental pathologies using faster-rcnn in orthopantomogram radiography image. In: 2020 7th international conference on signal processing and integrated networks (SPIN). IEEE, pp 423–428

  20. Lee J-H, Kim D-H, Jeong S-N, Choi S-H (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry 77:106–111

    Article  Google Scholar 

  21. Lira PH, Giraldi GA, Neves LA (2009) Panoramic dental x-ray image segmentation and feature extraction. In: Proceedings of V workshop of computing vision, Sao Paulo, Brazil

  22. Majdabadi MM, Ko S-B (2020) Msg-capsgan: multi-scale gradient capsule gan for face super resolution. In: 2020 international conference on electronics, information, and communication (ICEIC). IEEE, pp 1–3

  23. Majdabadi MM, Ko S-B (2020) Capsule gan for robust face super resolution. Multimed Tools Appl :1–14

  24. Martínez-Rus F, García AM, de Aza AH, Pradíes G (2011) Radiopacity of zirconia-based all-ceramic crown systems. Int J Prosthodont 24:2

    Google Scholar 

  25. Naam J, Harlan J, Madenda S, Wibowo EP (2016) The algorithm of image edge detection on panoramic dental x-ray using multiple morphological gradient (mmg) method. International Journal on Advanced Science Engineering and Information Technology 6(6):1012–1018

    Article  Google Scholar 

  26. Nomir O, Abdel-Mottaleb M (2005) A system for human identification from x-ray dental radiographs. Pattern Recogn. 38(8):1295–1305

    Article  MATH  Google Scholar 

  27. Ølberg J-V, Goodwin M (2016) Automated dental identification with lowest cost path-based teeth and jaw separation. Scandinavian Journal of Forensic Science 22(2):44–56

    Article  Google Scholar 

  28. Oliveira J, Proença H (2011) Caries detection in panoramic dental x-ray images. In: Computational vision and medical image processing. Springer, pp 175–190

  29. Pal A, Chaturvedi A, Garain U, Chandra A, Chatterjee R, Senapati S (2018) Capsdemm: capsule network for detection of munro’s microabscess in skin biopsy images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 389–397

  30. Pitts N, Zero D (2016) White paper on dental caries prevention and management. FDI World Dental Federation

  31. Qu X, Li G, Zhang Z, Ma X (2011) Detection accuracy of in vitro approximal caries by cone beam computed tomography images. Eur J Radiol 79 (2):e24–e27

    Article  Google Scholar 

  32. Rad AE, Rahim MSM, Kolivand H, Norouzi A (2018) Automatic computer-aided caries detection from dental x-ray images using intelligent level set. Multimed Tools Appl 77(21):28843–28862

    Article  Google Scholar 

  33. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K et al (2017) Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning, arXiv:1711.05225

  34. Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. arXiv:1710.05941

  35. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems 30, Curran Associates, Inc., pp 3856–3866. http://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdf

  36. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

  37. Selwitz RH, Ismail AI, Pitts N (2007) Dental caries. Lancet 369(9555):51–59

    Article  Google Scholar 

  38. Sheta A, Braik MS, Aljahdali S (2012) Genetic algorithms: a tool for image segmentation. In: 2012 international conference on multimedia computing and systems. IEEE, pp 84–90

  39. Silva G, Oliveira L, Pithon M (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 

  40. Smith LN (2017) Cyclical learning rates for training neural networks. In: 2017 IEEE winter conference on applications of computer vision (WACV) IEEE, pp 464–472

  41. Srivastava MM, Kumar P, Pradhan L, Varadarajan S (2017) Detection of tooth caries in bitewing radiographs using deep learning. arXiv:1711.07312

  42. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  43. Tagliaferro E, Junior AV, Rosell FL, Silva S, Riley JL, Gilbert GH, Gordan VV (2019) Caries diagnosis in dental practices: Results from dentists in a brazilian community. Oper Dent 44(1):E23–E31

    Article  Google Scholar 

  44. Tang B, Li A, Li B, Wang M (2019) Capsurv: capsule network for survival analysis with whole slide pathological images. IEEE Access 7:26022–26030

    Article  Google Scholar 

  45. Zhao T, Liu Y, Huo G, Zhu X (2019) A deep learning iris recognition method based on capsule network architecture. IEEE Access 7:49691–49701

    Article  Google Scholar 

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    Authors and Affiliations

    1. Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan, SK, Canada

      Arman Haghanifar & Seok-Bum Ko

    2. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatchewan, SK, Canada

      Mahdiyar Molahasani Majdabadi & Seok-Bum Ko

    3. Department of Oral Maxillofacial Radiology, Dental Faculty, Babol University of Medical Sciences, Babol, Iran

      Sina Haghanifar

    4. International Road Dynamics, Saskatchewan, SK, Canada

      Younhee Choi

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    1. Arman Haghanifar
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    Correspondence to Seok-Bum Ko.

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    Arman Haghanifar and Mahdiyar Molahasani Majdabadi contributed equally to this work.

    This work is the extended version of “Automated Teeth Extraction from Dental Panoramic X-Ray Images using Genetic Algorithm,” [15] in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, Oct. 2020.

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    Haghanifar, A., Majdabadi, M.M., Haghanifar, S. et al. PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier. Multimed Tools Appl 82, 27659–27679 (2023). https://doi.org/10.1007/s11042-023-14435-9

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