Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using two-dimensional (2D) segmentation (sagittal or axial planes) can calculate bone mineral density (BMD) and predict osteoporosis-related outcomes. Purpose...
Purpose To investigate whether the computational effort of three-dimensional CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, ...
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2025
During transfer tasks, the dual-arm nursing-care robot require a human-robot mechanics model to determine the balance region to support the patient safely and stably. Previous studies utilized human-robot two-dimensional static equilibrium models, ig...
Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
Feb 18, 2025
OBJECTIVE: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynam...
BACKGROUND: Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access soft...
Translational vision science & technology
Feb 3, 2025
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...
OBJECTIVE: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality.
Deep learning has revolutionized the classification of land cover objects in hyperspectral images (HSIs), particularly by managing the complex 3D cube structure inherent in HSI data. Despite these advances, challenges such as data redundancy, computa...
To address the challenges of significant detail loss in Neural Radiance Fields (NeRF) under sparse-view input conditions, this paper proposes the DASNeRF framework. DASNeRF aims to generate high-detail novel views from a limited number of input viewp...
OBJECTIVE: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.
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