AIMC Topic: Cone-Beam Computed Tomography

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A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study.

Journal of dentistry Oct 26, 2021
OBJECTIVES: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning appr...
Deep Learning Cone-Beam Computed Tomography Artificial Intelligence Image Processing, Computer-Assisted Tooth
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A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections.

Physics in medicine and biology Oct 22, 2021
Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to ...
Humans Neural Networks, Computer Artifacts Image Processing, Computer-Assisted Phantoms, Imaging Cone-Beam Computed Tomography
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Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
Humans Neural Networks, Computer Prognosis Phantoms, Imaging Radiotherapy Dosage Radiotherapy, Intensity-Modulated Lung Neoplasms Bone Neoplasms Cone-Beam Computed Tomography Organs at Risk Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Soft Tissue Neoplasms Deep Learning
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A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography.

Journal of endodontics Sep 24, 2021
INTRODUCTION: The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular ...
Mandible Molar Tooth Root Dental Pulp Cavity Deep Learning Cone-Beam Computed Tomography
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Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis.

Journal of healthcare engineering Sep 21, 2021
In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm f...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Deep Learning Tooth Humans Cone-Beam Computed Tomography Algorithms
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Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection.

Journal of healthcare engineering Sep 21, 2021
This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, c...
Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Algorithms Research Subjects Deep Learning Neural Networks, Computer
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Integration of imaging modalities in digital dental workflows - possibilities, limitations, and potential future developments.

Dento maxillo facial radiology Sep 14, 2021
The digital workflow process follows different steps for all dental specialties. However, the main ingredient for the diagnosis, treatment planning and follow-up workflow recipes is the imaging chain. The steps in the imaging chain usually include al...
Workflow Humans Artificial Intelligence Cone-Beam Computed Tomography Imaging, Three-Dimensional Computer-Aided Design
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Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography.

Journal of endodontics Sep 11, 2021
INTRODUCTION: This study proposes a novel data pipeline based on micro-computed tomographic (micro-CT) data for training the U-Net network to realize the automatic and accurate segmentation of the pulp cavity and tooth on cone-beam computed tomograph...
Artificial Intelligence X-Ray Microtomography Dental Pulp Cavity Tooth Cone-Beam Computed Tomography Image Processing, Computer-Assisted
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Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance.

Medical physics Aug 30, 2021
PURPOSE: Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C-arm system. However, the projective data provide only limited information about the spatial structur...
Deep Learning Cone-Beam Computed Tomography Fluoroscopy Phantoms, Imaging Image Processing, Computer-Assisted Humans Tomography, X-Ray Computed X-Rays
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Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography.

Journal of dentistry Aug 20, 2021
OBJECTIVE: To develop and validate a layered deep learning algorithm which automatically creates three-dimensional (3D) surface models of the human mandible out of cone-beam computed tomography (CBCT) imaging.
Artificial Intelligence Mandible Deep Learning Image Processing, Computer-Assisted Cone-Beam Computed Tomography Humans
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