KIEE - The Transactions of the Korean Institute of Electrical Engineers

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K. Stokbro, 2014, Virtual planning in orthognathic surgery, International journal of oral and maxillofacial surgery, Vol. 43, No. 8, pp. 957-965

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Sanjay Naran, Derek M. Steinbacher, Jesse A. Taylor, 2018, Current concepts in orthognathic surgery, Plastic and reconstructive surgery, 141.6:925e-936e

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Christos Angelopoulos, Tara Aghaloo, 2011, Imaging technology in implant diagnosis, Dental Clinics, Vol. 55.1, pp. 141-158

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Philip Worthington, Jeffrey Rubenstein, David C. Hatcher, 2010, The role of cone-beam computed tomography in the planning and placement of implants, The Journal of the American Dental Association, pp. 141:19s-24S

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Dae-Seung Kim, 2014, An integrated orthognathic surgery system for virtual planning and image-guided transfer without intermediate splint, Journal of Cranio-Maxillofacial Surgery, pp. 42.8:2010-2017

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Brian B. Farrell, 2014, Virtual surgical planning in orthognathic surgery, Oral and maxillofacial surgery clinics of North America, pp. 26.4:459-473

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Bart Vandenberghe, 2010, Modern dental imaging: a review of the current technology and clinical applications in dental practice, European radiology, pp. 20.11:2637-2655

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Michele Cassetta, 2014, How accurate Is CBCT in mea- suring bone density: A comparative CBCT-CT in vitro study, Clinical implant dentistry and related research, pp. 16.4: 471-478

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Timo Kiljunen, 2015, Dental cone beam CT: A review, Physica Medica, pp. 31.8:844-860

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Nagarajappa, 2015, Artifacts: The downturn of CBCT image, Journal of International Society of Preventive & Community Dentistry

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R. Schulze, 2011, Artefacts in CBCT: a review, Dento-maxillofacial Radiology, pp. 40.5:265-273

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Cosimo Nardi, 2014, Metal and motion artifacts by cone beam computed tomography (CBCT) in dental and maxil-lofacial study, La radiologia medica, pp. 120.7:618-626

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Joel Jaskari, 2020, Deep learning method for mandibular canal segmentation in dental cone beam computed tomo- graphy volumes, Scientific reports, pp. 10.1:1-8

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Seok-Ki Jung, 2021, Deep Active Learning for Automatic Segmentation of Maxillary Sinus Lesions Using a Convolu- tional Neural Network, Diagnostics, 11.4:688

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Jordi Minnema, 2019, Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network, Medical physics, pp. 46.11: 5027-5035

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Mantas Vaitieknas, 2020, Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set, Applied Sciences

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Joojin Kim, MIN JIN LEE, Helen Hong, 2017, Automatic Segmentation of the Mandible using Shape-Constrained Information in Cranio-Maxillo-Facial CBCT Images, Korea Computer Graphics Society, Vol. 23, No. 5, pp. 19-27

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Fatemeh Abdolali, 2017, Automatic segmentation of mandi- bular canal in cone beam CT images using conditional statistical shape model and fast marching, International journal of computer assisted radiology and surgery, pp. 12.4: 581-593

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Tae-Hoon Yong, 2021, QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study, Scientific Reports, pp. 11.1: 1-13

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Odeuk Kwon, 2020, Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network, Dentomaxillofacial Radiology, 49.8:20200185

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Hyuk-Joon Chang, 2020, Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis, Scientific reports, pp. 10.1:1-8

22

Su Yang, 2019, Deep learning segmentation of major vessels in X-ray coronary angiography, Scientific reports, pp. 9.1:1-11

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Olaf Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention

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Gao Huang, 2017, Densely connected convolutional net- works, Proceedings of the IEEE conference on computer vision and pattern recognition

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Karen Simonyan, Andrew Zisserman, 2014, Very deep con-volutional networks for large-scale image recognition, arXiv preprint, arXiv:1409.1556

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Kaiming He, 2016, Deep residual learning for image recognition, Proceedings of the IEEE conference on compu- ter vision and pattern recognition

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Mingxing Tan, Quoc Le, 2019, Efficientnet: Rethinking model scaling for convolutional neural networks, International Conference on Machine Learning

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Nabila Abraham, Naimul Mefraz Khan, 2019, A novel focal tversky loss function with improved attention u-net for lesion segmentation, IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Su Yang, 2019, Major vessel segmentation on x-ray coronary angiography using deep networks with a novel penalty loss function, International Conference on Medical Imaging with Deep Learning—Extended Abstract Track

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Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour, 2017, Tversky loss function for image segmentation using 3D fully convolutional deep networks, International workshop on machine learning in medical imaging

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Diederik P. Kingma, Jimmy Ba, 2014, Adam: A method for stochastic optimization, arXiv preprint, arXiv:1412.6980

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Karl Weiss, 2016, A survey of transfer learning, Journal of Big data, pp. 3.1:1-40

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Abdel Aziz Taha, Allan Hanbury, 2015, Metrics for evalu- ating 3D medical image segmentation: analysis, selection, and tool, BMC medical imaging, pp. 15.1:1-28

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Salman Khan, 2021, Transformers in vision: A survey, arXiv preprint

KIEEThe Transactions of
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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers