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Found 14 papers, 0 papers with code
Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging
This study introduces a novel reconstruction method for dental cone-beam computed tomography (CBCT), focusing on effectively reducing metal-induced artifacts commonly encountered in the presence of prevalent metallic implants.
Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images
A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models.
Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods.
Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling
To improve the learning ability, the proposed network is designed to take advantage of the intra-oral scan data as side-inputs and perform multi-task learning of auxiliary tooth segmentation.
Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification
The proposed method is intended not only to compensate the low-quality of CBCT-derived tooth surfaces with IOS, but also to correct the cumulative stitching errors of IOS across the entire dental arch.
A fully automated method for 3D individual tooth identification and segmentation in dental CBCT
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone.
Automated 3D cephalometric landmark identification using computerized tomography
This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed.
Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging
Recently, with the significant developments in deep learning techniques, solving underdetermined inverse problems has become one of the major concerns in the medical imaging domain.
Framelet Pooling Aided Deep Learning Network : The Method to Process High Dimensional Medical Data
Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of dimensionality problem, and the generalization issues.
Unpaired image denoising using a generative adversarial network in X-ray CT
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data.
Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks
The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation.
Deep learning for undersampled MRI reconstruction
This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well.
CT sinogram-consistency learning for metal-induced beam hardening correction
This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time.
Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images
The proposed method automatically estimates AC from ultrasound images.
