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3D Ağız İçi Taramalarına ait Nokta Bulutu Verileri ile Derin Öğrenme Kullanılarak Diş Tipi Sınıflandırması

1Ondokuz Mayıs Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
✉ Corresponding author: İsmail İşeri · ismail.iseri@omu.edu.tr
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References

  1. R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, ‘‘PointNet: Deep learning on point sets for 3D classification and segmentation,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jan. 2017, pp. 77–85.
  2. C. Qi, L. Yi, H. Su, and L. Guibas, ‘‘PointNet++: Deep hierarchical feature learning on point sets in a metric space,’’ in Proc. Adv. Neural Inf. Process. Syst., Dec. 2017, pp. 5100–5109.
  3. P. Papadakis, I. Pratikakis, S. Perantonis, and T. Theoharis, ‘‘Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation,’’ Pattern Recognit., vol. 40, no. 9, pp. 2437–2452, Sep. 2007.
  4. Q. Huang, Y. Wang, and Z. Yin, ‘‘View-based weight network for 3D object recognition,’’ Image Vis. Comput., vol. 93, Jan. 2020, Art. no. 103828
  5. Poonsri, A., Aimjirakul, N., Charoenpong, T., & Sukjamsri, C. (2016). Teeth segmentation from dental x-ray image by template matching. 2016 9th Biomedical Engineering International Conference (BMEiCON), 1-4.
  6. Tian, S., Dai, N., Zhang, B., Yuan, F., Yu, Q., & Cheng, X. (2019). Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks. IEEE Access, 7, 84817-84828.
  7. Xu, X., Liu, C., & Zheng, Y. (2019). 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. IEEE Transactions on Visualization and Computer Graphics, 25, 2336-2348.
  8. O. Vinyals, S. Bengio, and M. Kudlur, ‘‘Order matters: Sequence to sequence for sets,’’ 2015, arXiv:1511.06391. [Online]. Available: http://arxiv.org/abs/1511.06391
  9. S. Ravanbakhsh, J. Schneider, and B. Poczos, ‘‘Deep learning with sets and point clouds,’’ 2016, arXiv:1611.04500.
  10. M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, ‘‘Deep sets,’’ in Proc. Adv. Neural Inf. Process. Syst., Dec. 2017, pp. 3392–3402.
  11. P. Daras and A. Axenopoulos, ‘‘A 3D shape retrieval framework supporting multimodal queries,’’ Int. J. Comput. Vis., vol. 89, nos. 2–3, pp. 229–247, Sep. 2010.
  12. Y. Yang, C. Feng, Y. Shen, and D. Tian, ‘‘FoldingNet: Point cloud autoencoder via deep grid deformation,’’ in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 206–215.
  13. J. Li, B. M. Chen, and G. H. Lee, ‘‘SO-net: Self-organizing network for point cloud analysis,’’ in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Salt Lake City, UT, USA, Jun. 2018, pp. 9397–9406.
  14. Miki, Y., Muramatsu, C., Hayashi, T., Zhou, X., Hara, T. Katsumata, A. & Fujita, H. (2017). Classification of teeth in cone-beam ct using deep convolutional neural network. In Computers in biology & medicine, vol. 80, pp. 24–29
  15. Tuzoff, D. V., Tuzova, L. N., Bornstein, M. M., Krasnov, A. S., Kharchenko, M. A., Nikolenko, S. I., Sveshnikov, M. M. Bednenko, & G. B. (2019). Tooth detection & numbering in panoramic radiographs using convolutional neural networks. In Dentomaxillofacial Radiology, vol. 48, no. 4, p. 20180051
  16. Wu, K., Chen, L., Li, J. & Zhou, Y. (2014). Tooth segmentation on dental meshes using morphologic skeleton. In Comput. Graph., vol. 38, no. 1, pp. 199–211
  17. Kumar, Y., Janardan, R., Larson, B. & Moon, J. (2011). Improved segmentation of teeth in dental models. In Comput.-Aided Des. Appl., vol. 8, no. 2, pp. 211–224
  18. Li, Z. & Wang, H. (2016). Interactive tooth separation from dental model using segmentation field. In PLoS ONE, vol. 11, no. 8, Art. no. e0161159.
  19. Lian, C., Wang, L., Wu, T., Liu, M., Durán, F., Ko, C., & Shen, D. (2019). MeshSNet: Deep Multi-scale Mesh Feature Learning for Endto-End Tooth Labeling on 3D Dental Surfaces. MICCAI.
  20. Ibáñez, L. (2014). STL file format MeshIO class for ITK. The Insight Journal.

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Research Contributions (2019+)Peer reviewed by at least two reviewers
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