AIMC Topic: Imaging, Three-Dimensional

Clear Filters Showing 1571 to 1580 of 1894 articles

ReSCU-Nets: Recurrent U-Nets for segmentation of three-dimensional microscopy data.

The Journal of cell biology
Segmenting multidimensional microscopy data requires high accuracy across many images (e.g., time points or Z slices) and is thus a labor-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural n...

MC-RED: A deep learning network for motion correction in 3D CEST imaging.

Magnetic resonance in medicine
PURPOSE: Chemical exchange saturation transfer (CEST) imaging is highly sensitive to patient motion, which can compromise the reliability of quantitative molecular analysis. This study aims to develop and validate a deep learning-based motion correct...

End-to-end 2D/3D registration from pre-operative MRI to intra-operative fluoroscopy for orthopedic procedures.

International journal of computer assisted radiology and surgery
PURPOSE: Soft tissue pathologies and bone defects are not easily visible in intra-operative fluoroscopic images; therefore, we develop an end-to-end MRI-to-fluoroscopic image registration framework, aiming to enhance intra-operative visualization for...

Training a deep learning model to predict the anatomy irradiated in fluoroscopic x-ray images.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient ...

Volumetric Medical Image Segmentation Through Dual Self-Distillation in U-Shaped Networks.

IEEE transactions on bio-medical engineering
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in U-shaped networks for volumetric medical image segmentation. DSD dist...

A Neighbor-Sensitive Multi-Modal Flexible Learning Framework for Improved Prostate Tumor Segmentation in Anisotropic MR Images.

IEEE transactions on bio-medical engineering
Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for the diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods ...

Single-View 3D Hair Modeling With Clumping Optimization.

IEEE transactions on visualization and computer graphics
Deep learning advancements have enabled the generation of visually plausible hair geometry from a single image, but the results still do not meet the realism required for further applications (e.g., high quality hair rendering and simulation). One of...

Advances in the Application of Three-Dimensional Reconstruction in Thoracic Surgery: A Comprehensive Review.

Thoracic cancer
This review presents a comprehensive overview of recent advancements and clinical applications of three-dimensional (3D) reconstruction technology in thoracic surgery, with a focus on lung cancer surgery. The widespread adoption of chest computed tom...

Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T.

NMR in biomedicine
To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model t...

Deep sensorless tracking of ultrasound probe orientation during freehand transperineal biopsy with spatial context for symmetry disambiguation.

Computers in biology and medicine
BACKGROUND: Diagnosis of prostate cancer requires histopathology of tissue samples. Following an MRI to identify suspicious areas, a biopsy is performed under ultrasound (US) guidance. In existing assistance systems, 3D US information is generally av...