Automatic design and 3D segmentation of mandible bone using CNN algorithm via exclusive GUI
الموضوعات :
Nadia MehradKia
1
(Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University,
Isfahan, Iran.)
Shakiba Mohammadi
2
(Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University,
Isfahan, Iran.)
Sayedali Mousavi
3
(Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University,
Isfahan, Iran.)
الکلمات المفتاحية: Segmentation, FCN, Mandible, Alveolar neural canal, CBCT, U-net,
ملخص المقالة :
Nowadays scientists are looking for decreasing dental faults by presenting new approaches.It is obvious that comprehensive information about the anatomic position of the inferior alveolar neuralcanal is essential to have the most ideal mandible surgery or systemic tooth implant. Accordingly, wepresent a new approach in this article that can be used to have 3D segmentation and recognition of thementioned canal in mandible by CBCT image. This approach includes two main steps. In the first step,we train a full convolutional 3D net (FCN) to reach the ability of section recognition, which can recognizethe relevant area of mandible bone. And in the next step, we define a 3D U-net, which is similar to FCN,to segment the inferior Alveolar neural (IAN) canal from the lower jaw. Evaluated on publicly availabledatasets, our method achieved an average Dice coefficient of 86.61%.
K. Madan, S. Baliga, N. Thosar, and N. J. J. o. M. Rathi, "Recent advances in dental radiography for pediatric patients: A review," Medicine, Radiology, Pathology & Surgery, vol. 1, pp. 21-25, 2015.
[2] R. Lloréns, V. Naranjo, M. Clemente, M. A. Raya, and S. Albalat, "FC-based Segmentation of Jaw Tissues," in BIOSIGNALS, pp. 409-414, 2010.
[3] D.-J. Kroon, Segmentation of the mandibular canal in cone-beam CT data. Citeseer, 2011.
[4] C. Keatmanee, S. S. Makhanov, K. Kotani, T. Kondo, and S. S. Thongvigitmanee, "Inferior alveolar canal segmentation in cone beam computed tomography images using an adaptive diffusion flow active contour model," 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 57-60, 2015.
[5] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," MICCAI, vol. 9351, pp. 234-241, 2015.
[6] M. Wang, J. He, Y. Liu, M. Li, D. Li, and Z. J. I. J. o. B. Jin, "The trend towards in vivo bioprinting," International Journal of Bioprinting, vol. 1, no. 1, 2015.
[7] S. Lee, S. Woo, J. Yu, J. Seo, J. Lee, and C. J. I. A. Lee, "Automated CNN-Based tooth segmentation in cone-beam CT for dental implant planning," IEEE Access, vol. 8, pp. 50507-50518, 2020.
[8] H. Kaur and J. Rani, "MRI brain image enhancement using Histogram equalization Techniques," International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), vol. 11, pp. 770-773, 2016.
[9] Emer J. Hughes, Tobias Winchman, Francesco Padormo,1 Rui Teixeira,1 Julia Wurie, Maryanne Sharma,1 Matthew Fox,1 Jana Hutter, Lucilio Cordero-Grande,1 Anthony N. Price, Joanna Allsop, Jose Bueno-Conde,1 Nora Tusor,1 Tomoki Arichi,1 A. D. Edwards,1 Mary A. Rutherford, Serena J. Counsell, and Joseph V. Hajnal “A Dedicated Neonatal Brain Imaging System,” Magnetic Resonance in Medicine, vol. 78, pp. 794–804, 2017.
[10] D. Štern, T. Ebner, and M. Urschler, "From local to global random regression forests: Exploring anatomical landmark localization," International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. vol. 8674, pp. 221-229, , 2016.
[11] A. Suzani, A. Seitel, Y. Liu, S. Fels, R. N. Rohling, and P. Abolmaesumi, "Fast automatic vertebrae detection and localization in pathological CT scans-a deep learning approach," MICCAI, Springer. vol. 9351, pp. 678-686, , 2015.
[12] K. Simonyan and A. J. a. p. a. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv, vol. 6, pp.1-14, 2014.
[13] J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, "Unitbox: An advanced object detection network," Proceedings of the 24th ACM international conference on Multimedia, vol. 127, pp. 516-520, 2016.
[14] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, "3D U-Net: learning dense volumetric segmentation from sparse annotation," MICCAI, vol. 9901, pp. 424-432, 2016.
[15] B. Qiu, J. Guo, J. Kraeima, H.H. Glas, R.J.H. Borra, M.J.H. Witjes, and P.M.A. van Ooijen"Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network," Physics in Medicine & Biology, vol. 64, pp. 175020 (1-13), 2019.
