AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Imaging, Three-Dimensional

Showing 151 to 160 of 1612 articles

Clear Filters

Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment.

Magnetic resonance imaging
PURPOSES: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of ...

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

Medical physics
BACKGROUND: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these...

Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.

JCO clinical cancer informatics
PURPOSE: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VB...

Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

IEEE journal of biomedical and health informatics
Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided interventions. Deep Learning (DL) methods have become the state-of-the-art for several image segmen...

Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.

IEEE journal of biomedical and health informatics
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of traine...

Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes.

IEEE transactions on medical imaging
The ability to recover tissue deformation from surgical video is fundamental for many downstream applications in robotic surgery. Despite noticeable advancements, this task remains under-explored due to the complex dynamics of soft tissues manipulate...

Real-time 3D MR guided radiation therapy through orthogonal MR imaging and manifold learning.

Medical physics
BACKGROUND: In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a p...

Deep learning versus human assessors: forensic sex estimation from three-dimensional computed tomography scans.

Scientific reports
Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which ...

Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

PloS one
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning,...

Multitask learning for automatic detection of meniscal injury on 3D knee MRI.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Magnetic resonance imaging (MRI) of the knee is the recommended diagnostic method before invasive arthroscopy surgery. Nevertheless, interpreting knee MRI scans is a time-consuming process that is vulnerable to inaccuracies and inconsistencies. We pr...