Previous Articles Next Articles
SHENG Chen1,2, WANG Lin1,2,3, HUANG Zhenhuan1,2,3, WANG Tian1,2,3, GUO Yalin1,2,3, HOU Wenjie1,2,3, XU Laiqing1,2,3, WANG Jiazhu1,2,3, YAN Xue1,2,3
|  Perschbacher S, Interpretation of panoramic radiographs, Australian Dental Journal, 2012, 57:40-45.
 Kim J, Kim H, and Ro Y, Iterative deep convolutional encoder-decoder network for medical image segmentation, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, 685-688.
 Zhao J, Ma Y, Pan Z, et al., Research on image signal identification based on adaptive array stochastic resonance, Journal of Systems Science and Complexity, 2022, 35(1):179-193.
 Wu C, Tsai W, Chen Y, et al., Model-based orthodontic assessments for dental panoramic radiographs, IEEE Journal of Biomedical and Health Informatics, 2017, 22(2):545-551.
 Ammar H, Ngan P, Crout R, et al., Three-dimensional modeling and finite element analysis in treatment planning for orthodontic tooth movement, American Journal of Orthodontics and Dentofacial Orthopedics, 2011, 139(1):59-71.
 Jiang Y, Qian J, Lu S, et al., LRVRG:A local region-based variational region growing algorithm for fast mandible segmentation from cbct images, Oral Radiology, 2021, 37(4):631-640.
 Wang T, Qiao M, Zhang M, et al., Data-driven prognostic method based on self-supervised learning approaches for fault detection, Journal of Intelligent Manufacturing, 2020, 31(7):1611-1619.
 Razali M, Ahmad N, Hassan R, et al., Sobel and canny edges segmentations for the dental age assessment, Proceedings of International Conference on Computer Assisted System in Health, 2014, 62-66.
 Pérez-Benito F, Signol F, Perez-Cortes J, et al., A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation, Computer Methods and Programs in Biomedicine, 2020, 195:105668.1-36.
 Bergeest J and Rohr K, Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals, Medical Image Analysis, 2012, 16(7):1436-1444.
 Gong X, Chen S, Zhang B, et al., Style consistent image generation for nuclei instance segmentation, Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021, 3994-4003.
 Mao M, Gao P, Zhang R, et al., Dual-stream network for visual recognition, Proceeings of Advances in Neural Information Processing Systems, 2021, 34-46.
 Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639):115-118.
 Wang T, Qiao M, Lin Z, et al., Generative neural networks for anomaly detection in crowded scenes, IEEE Transactions on Information Forensics and Security, 2018, 14(5):1390-1399.
 Leite A F, Van Gerven A, Willems H, et al., Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs, Clinical Oral Investigations, 2021, 25(4):2257-2267.
 Vinayahalingam S, Xi T, Bergé S, et al., Automated detection of third molars and mandibular nerve by deep learning, Scientific Reports, 2019, 9(1):1-7.
 Xu X, Liu C, and Zheng Y, 3D tooth segmentation and labeling using deep convolutional neural networks, IEEE Transactions on Visualization and Computer Graphics, 2018, 25(7):2336-2348.
 Van Eycke Y, Foucart A, and Decaestecker C, Strategies to reduce the expert supervision required for deep learning-based segmentation of histopathological images, Frontiers in Medicine, 2019, 6:222-231.
 Miotto R, Wang F, Wang S, et al., Deep learning for healthcare:Review, opportunities and challenges, Briefings in Bioinformatics, 2018, 19(6):1236-1246.
 Liu P, Song Y, Chai M, et al., Swin-unet++:A nested swin transformer architecture for location identification and morphology segmentation of dimples on 2.25 cr1mo0. 25v fractured surface, Materials, 2021, 14(24):7504.1-15.
 Luo C, Zhang J, Chen X, et al., UCATR:Based on CNN and transformer encoding and crossattention decoding for lesion segmentation of acute ischemic stroke in non-contrast computed tomography images, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2021, 2021:3565-3568.
 Wang C, Huang C, Lee J, et al., A benchmark for comparison of dental radiography analysis algorithms, Medical Image Analysis, 2016, 31(24):63-76.
 Wirtz A, Mirashi S G, and Wesarg S, Automatic teeth segmentation in panoramic x-ray images using a coupled shape model in combination with a neural network, Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention, 2018, 712-719.
 Chan H, Samala R, Hadjiiski L, et al., Deep learning in medical image analysis, Deep Learning in Medical Image Analysis, 2020, 1213:3-21.
 Schwendicke F, Golla T, Dreher M, et al., Convolutional neural networks for dental image diagnostics:A scoping review, Journal of Dentistry, 2019, 91:103226.1-8.
 Goodfellow I, Bengio Y, and Courville A, Deep Learning, MIT Press, Cambridge, 2016.
 Zhang Y, Zhang S, Li Y, et al., Single-and cross-modality near duplicate image pairs detection via spatial transformer comparing CNN, Sensors (Basel), 2021, 21(1):255.
 Zhou Z, Siddiquee M M R, Tajbakhsh N, et al., Unet++:A nested u-net architecture for medical image segmentation, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2018, 11045:3-11.
 Krois J, Ekert T, Meinhold L, et al., Deep learning for the radiographic detection of periodontal bone loss, Scientific Reports, 2019, 9(1):1-6.
 Chaurasia A and Culurciello E, Linknet:Exploiting encoder representations for efficient semantic segmentation, Proceedings of IEEE Visual Communications and Image Processing, 2017, 1-4.
 Arora R, Saini I, and Sood N, Multi-label segmentation and detection of covid-19 abnormalities from chest radiographs using deep learning, Optik, 2021, 246:167780.1-18.
 Lin T, Dollár P, Girshick R, et al., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 2117-2125.
 Lahoud P, EzEldeen M, Beznik T, et al., Artificial intelligence for fast and accurate 3-dimensional tooth segmentation on cone-beam computed tomography, Journal of Endodontics, 2021, 47(5):827-835.
 Nishitani Y, Nakayama R, Hayashi D, et al., Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge, Radiol Phys. Technol., 2021, 14(1):64-69.
 Silva G, Oliveira L, and Pithon M, Automatic segmenting teeth in x-ray images:Trends, a novel data set, benchmarking and future perspectives, Expert Systems with Applications, 2018, 10715-31.
|No related articles found!|