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Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net

Published:October 31, 2022DOI:https://doi.org/10.1016/j.ijom.2022.10.015

      Abstract

      The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multiple areas. In this study, a U-Net was used to investigate the automatic segmentation of facial bones into eight areas, with the aim of facilitating virtual surgical planning (VSP) and computer-aided design and manufacturing (CAD/CAM) in maxillofacial surgery. CT data from 50 patients were prepared and used for training, and five-fold cross-validation was performed. The output results generated by the DL model were validated by Dice coefficient and average symmetric surface distance (ASSD). The automatic segmentation was successful in all cases, with a mean± standard deviation Dice coefficient of 0.897 ± 0.077 and ASSD of 1.168 ± 1.962 mm. The accuracy was very high for the mandible (Dice coefficient 0.984, ASSD 0.324 mm) and zygomatic bones (Dice coefficient 0.931, ASSD 0.487 mm), and these could be introduced for VSP and CAD/CAM without any modification. The results for other areas, particularly the teeth, were slightly inferior, with possible reasons being the effects of defects, bonded maxillary and mandibular teeth, and metal artefacts. A limitation of this study is that the data were from a single institution. Hence further research is required to improve the accuracy for some facial areas and to validate the results in larger and more diverse populations.

      Keywords

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      References

        • Liu B.
        • Chi W.
        • Li X.
        • Li P.
        • Liang W.
        • Liu H.
        • Wang W.
        • He J.
        Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect.
        J Cancer Res Clin Oncol. 2020; 146: 153-185
        • Ström P.
        • Kartasalo K.
        • Olsson H.
        • Solorzano L.
        • Delahunt B.
        • Berney D.M.
        • Bostwick D.G.
        • Evans A.J.
        • Grignon D.J.
        • Humphrey P.A.
        • Iczkowski K.A.
        • Kench J.G.
        • Kristiansen G.
        • van der Kwast T.H.
        • Leite K.R.M.
        • McKenney J.K.
        • Oxley J.
        • Pan C.C.
        • Samaratunga H.
        • Srigley J.R.
        • Takahashi H.
        • Tsuzuki T.
        • Varma M.
        • Zhou M.
        • Lindberg J.
        • Lindskog C.
        • Ruusuvuori P.
        • Wählby C.
        • Grönberg H.
        • Rantalainen M.
        • Egevad L.
        • Eklund M.
        Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.
        Lancet Oncol. 2020; 21: 222-232
        • Tanaka H.
        • Chiu S.W.
        • Watanabe T.
        • Kaoku S.
        • Yamaguchi T.
        Computer-aided diagnosis system for breast ultrasound images using deep learning.
        Phys Med Biol. 2019; 64235013
        • Anwar S.M.
        • Majid M.
        • Qayyum A.
        • Awais M.
        • Alnowami M.
        • Khan M.K.
        Medical image analysis using convolutional neural networks: a review.
        J Med Syst. 2018; 42226
        • Day K.M.
        • Kelley P.K.
        • Harshbarger R.J.
        • Dorafshar A.H.
        • Kumar A.R.
        • Steinbacher D.M.
        • Patel P.
        • Combs P.D.
        • Levine J.P.
        Advanced three-dimensional technologies in craniofacial reconstruction.
        Plast Reconstr Surg. 2021; 148: 94e-108e
        • Padilla P.L.
        • Mericli A.F.
        • Largo R.D.
        • Garvey P.B.
        Computer-aided design and manufacturing versus conventional surgical planning for head and neck reconstruction: a systematic review and meta-analysis.
        Plast Reconstr Surg. 2021; 148: 183-192
        • Morita D.
        • Numajiri T.
        • Nakamura H.
        • Tsujiko S.
        • Sowa Y.
        • Yasuda M.
        • Hirano S.
        Intraoperative change in defect size during maxillary reconstruction using surgical guides created by CAD/CAM.
        Plast Reconstr Surg Glob Open. 2017; 5e1309
        • Morita D.
        • Numajiri T.
        • Tsujiko S.
        • Nakamura H.
        • Yamochi R.
        • Sowa Y.
        • Yasuda M.
        • Hirano S.
        Secondary maxillary and orbital floor reconstruction with a free scapular flap using cutting and fixation guides created by computer-aided design/computer-aided manufacturing.
        J Craniofac Surg. 2017; 28: 2060-2062
        • Numajiri T.
        • Morita D.
        • Nakamura H.
        • Tsujiko S.
        • Yamochi R.
        • Sowa Y.
        • Toyoda K.
        • Tsujikawa T.
        • Arai A.
        • Yasuda M.
        • Hirano S.
        Using an in-house approach to computer-assisted design and computer-aided manufacturing reconstruction of the maxilla.
        J Oral Maxillofac Surg. 2018; 76: 1361-1369
        • Numajiri T.
        • Morita D.
        • Nakamura H.
        • Yamochi R.
        • Tsujiko S.
        • Sowa Y.
        Designing CAD/CAM surgical guides for maxillary reconstruction using an in-house approach.
        J Vis Exp. 2018; 13858015
        • Numajiri T.
        • Morita D.
        • Yamochi R.
        • Nakamura H.
        • Tsujiko S.
        • Sowa Y.
        • Toyoda K.
        • Tsujikawa T.
        • Arai A.
        • Hirano S.
        Does an in-house computer-aided design/computer-aided manufacturing approach contribute to accuracy and time shortening in mandibular reconstruction?.
        J Craniofac Surg. 2020; 31: 1928-1932
        • Numajiri T.
        • Nakamura H.
        • Sowa Y.
        • Nishino K.
        Low-cost design and manufacturing of surgical guides for mandibular reconstruction using a fibula.
        Plast Reconstr Surg Glob Open. 2016; 4e805
        • Hiasa Y.
        • Otake Y.
        • Takao M.
        • Ogawa T.
        • Sugano N.
        • Sato Y.
        Automated muscle segmentation from clinical CT using Bayesian U-Net for personalized musculoskeletal modeling.
        IEEE Trans Med Imaging. 2019; 39: 1030-1040
        • Chang H.H.
        • Zhuang A.H.
        • Valentino D.J.
        • Chu W.C.
        Performance measure characterization for evaluating neuroimage segmentation algorithms.
        Neuroimage. 2009; 47: 122-135
        • Ghafoorian M.
        • Karssemeijer N.
        • Heskes T.
        • van Uden I.W.M.
        • Sanchez C.I.
        • Litjens G.
        • de Leeuw F.E.
        • van Ginneken B.
        • Marchiori E.
        • Platel B.
        Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities.
        Sci Rep. 2017; 75110
        • Tong N.
        • Gou S.
        • Yang S.
        • Ruan D.
        • Sheng K.
        Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
        Med Phys. 2018; 45: 4558-4567
        • Warfield S.K.
        • Zou K.H.
        • Wells W.M.
        Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.
        IEEE Trans Med Imaging. 2004; 23: 903-921
        • Yeghiazaryan V.
        • Voiculescu I.
        Family of boundary overlap metrics for the evaluation of medical image segmentation.
        J Med Imaging. 2018; 5015006
        • Ronneberger O.
        • Fischer P.
        • Brox T.
        U-Net: convolutional networks for biomedical image segmentation.
        in: Navab N. Hornegger J. Wells W. Frangi A. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer, 2015: 234-241
        • Wallner J.
        • Mischak I.
        • Jan E.
        Computed tomography data collection of the complete human mandible and valid clinical ground truth models.
        Sci Data. 2019; 6190003
        • Qiu B.
        • van der Wel H.
        • Kraeima J.
        • Glas H.H.
        • Guo J.
        • Borra R.J.H.
        • Witjes M.J.H.
        • van Ooijen P.M.A.
        Automatic segmentation of mandible from conventional methods to deep learning—a review.
        J Pers Med. 2021; 11629
        • Yan M.
        • Guo J.
        • Tian W.
        • Yi Z.
        Symmetric convolutional neural network for mandible segmentation.
        Knowl Based Syst. 2018; 159: 63-71
        • Xue J.
        • Wang Y.
        • Kong D.
        • Wu F.
        • Yin A.
        • Qu J.
        • Liu X.
        Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images.
        Expert Syst Appl. 2021; 168114446
        • Lei W.
        • Mei H.
        • Sun Z.
        • Ye S.
        • Gu R.
        • Wang H.
        • Huang R.
        • Zhang S.
        • Zhang S.
        • Wang G.
        Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss.
        Neurocomputing. 2021; 442: 184-199
        • Yang W.F.
        • Su Y.X.
        Artificial intelligence-enabled automatic segmentation of skull CT facilitates computer-assisted craniomaxillofacial surgery.
        Oral Oncol. 2021; 118105360
        • Sakamoto M.
        • Hiasa Y.
        • Otake Y.
        • Takao M.
        • Suzuki Y.
        • Sugano N.
        • Sato Y.
        Bayesian segmentation of hip and thigh muscles in metal artifact-contaminated CT using convolutional neural network-enhanced normalized metal artifact reduction.
        J Signal Process Syst. 2020; 92: 335-344
        • Nakao M.
        • Imanishi K.
        • Ueda N.
        • Imai Y.
        • Kirita T.
        • Matsuda T.
        Regularized three-dimensional generative adversarial nets for unsupervised metal artifact reduction in head and neck CT images.
        IEEE Access. 2020; 8: 109453-109465