Deep learning-based dental implant recognition using synthetic X-ray images

[1]  Young Hyun Kim,et al.  Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study , 2022, Imaging science in dentistry.

[2]  K. Kaliyaperumal,et al.  Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis , 2022, Security and Communication Networks.

[3]  Mallikarjun Anandhalli,et al.  Machine learning for identification of dental implant systems based on shape – A descriptive study , 2021, Journal of Indian Prosthodontic Society.

[4]  A. Darzi,et al.  Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis , 2021, npj Digital Medicine.

[5]  G. Wainrib,et al.  Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients , 2021, Nature Communications.

[6]  D. Álvarez,et al.  Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use , 2021, npj Digital Medicine.

[7]  Eduardo José da S. Luz,et al.  Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images , 2020, Research on Biomedical Engineering.

[8]  M. Le Roux,et al.  Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph , 2020, The International Journal of Oral & Maxillofacial Implants.

[9]  Jae‐Hong Lee,et al.  A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study , 2020, Diagnostics.

[10]  Takeshi Hara,et al.  Deep Neural Networks for Dental Implant System Classification , 2020, Biomolecules.

[11]  Matthias Karl,et al.  In Vitro Characterization of Original and Nonoriginal Implant Abutments. , 2018, The International journal of oral & maxillofacial implants.

[12]  Birgi Tamersoy,et al.  Generating Synthetic X-Ray Images of a Person from the Surface Geometry , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Xinqi Gong,et al.  Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images , 2018, BMC Syst. Biol..

[14]  Varun Jampani,et al.  Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Wim Dewulf,et al.  Industrial X-Ray Computed Tomography , 2018 .

[16]  Yun Wu,et al.  A model for fine-grained vehicle classification based on deep learning , 2017, Neurocomputing.

[17]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[18]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[19]  Karin Schwab,et al.  Medical Imaging Signals And Systems , 2016 .

[20]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[21]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[22]  Jaime C. Fonseca,et al.  Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation , 2015, Medical Imaging.

[23]  Pedro Morais,et al.  Computer-aided recognition of dental implants in X-ray images , 2015, Medical Imaging.

[24]  Vincent Lepetit,et al.  On rendering synthetic images for training an object detector , 2014, Comput. Vis. Image Underst..

[25]  Hilde Bosmans,et al.  Automated implant segmentation in cone-beam CT using edge detection and particle counting , 2014, International Journal of Computer Assisted Radiology and Surgery.

[26]  Miguel Ángel Guevara-López,et al.  A method for segmentation of dental implants and crestal bone , 2013, International Journal of Computer Assisted Radiology and Surgery.

[27]  Himanshu Aggarwal,et al.  A Comprehensive Review of Image Enhancement Techniques , 2010, ArXiv.

[28]  Virginie Busignies,et al.  Quantitative measurements of localized density variations in cylindrical tablets using X-ray microtomography. , 2006, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[29]  Ben M. Herbst,et al.  Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model , 2004, EURASIP J. Adv. Signal Process..

[30]  G. Deng,et al.  An adaptive Gaussian filter for noise reduction and edge detection , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[31]  Hanan Samet,et al.  Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  A. Jain,et al.  A Fast Karhunen-Loeve Transform for a Class of Random Processes , 1976, IEEE Trans. Commun..