AIMC Topic: Imaging, Three-Dimensional
Clear Filters Showing 271 to 280 of 1716 articles
Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.
Journal of imaging informatics in medicine
Sep 12, 2024
This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar...
Skeleton-guided 3D convolutional neural network for tubular structure segmentation.
International journal of computer assisted radiology and surgery
Sep 12, 2024
PURPOSE: Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network...
Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Sep 11, 2024
BACKGROUND: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated ...
Tetralogy of Fallot
Ventricular Function, Left
Magnetic Resonance Imaging, Cine
Deep Learning
Ventricular Function, Right
Humans
Adolescent
Child
Cardiac Surgical Procedures
Young Adult
Heart Ventricles
Imaging, Three-Dimensional
Adult
Image Interpretation, Computer-Assisted
Databases, Factual
Male
Treatment Outcome
Automation
Retrospective Studies
Predictive Value of Tests
Female
Reproducibility of Results
PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.
Journal of imaging informatics in medicine
Sep 9, 2024
PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ens...
Accelerating segmentation of fossil CT scans through Deep Learning.
Scientific reports
Sep 9, 2024
Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably ext...
Automated condylar seating assessment using a deep learning-based three-step approach.
Clinical oral investigations
Sep 4, 2024
OBJECTIVES: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated c...
FPL+: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.
IEEE transactions on medical imaging
Sep 3, 2024
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are ne...
Automated classification of mandibular canal in relation to third molar using CBCT images.
F1000Research
Sep 2, 2024
BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the...
Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.
Journal of sports science & medicine
Sep 1, 2024
OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and hu...
An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.
International journal of surgery (London, England)
Sep 1, 2024
BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization ...
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