AIMC Topic: Imaging, Three-Dimensional

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Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: To evaluate the feasibility of folded image training strategy (FITS) and the quality of images reconstructed using the improved model-based deep learning (iMoDL) network trained with FITS (FITS-iMoDL) for abdominal MR imaging.

Mixture 2D Convolutions for 3D Medical Image Segmentation.

International journal of neural systems
Three-dimensional (3D) medical image segmentation plays a crucial role in medical care applications. Although various two-dimensional (2D) and 3D neural network models have been applied to 3D medical image segmentation and achieved impressive results...

Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning.

Clinical oral investigations
OBJECTIVES: The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions.

Classification of Holograms with 3D-CNN.

Sensors (Basel, Switzerland)
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed i...

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

IEEE transactions on visualization and computer graphics
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functio...

Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud.

Sensors (Basel, Switzerland)
Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative m...

Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation.

Skeletal radiology
OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the imp...

Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning.

Ultrasound in medicine & biology
There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral reso...

Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears?

PloS one
BACKGROUND/PURPOSE: The use of MRI as a diagnostic tool has gained popularity in the field of orthopedics. Although 3-dimensional (3D) MRI offers more intuitive visualization and can better facilitate treatment planning than 2-dimensional (2D) MRI, m...

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highligh...