AIMC Topic: Image Processing, Computer-Assisted

Clear Filters Showing 4151 to 4160 of 10288 articles

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

Magnetic resonance in medicine
PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

Deep neural network automated segmentation of cellular structures in volume electron microscopy.

The Journal of cell biology
Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with...

Automatic lower limb bone segmentation in radiographs with different orientations and fields of view based on a contextual network.

International journal of computer assisted radiology and surgery
PURPOSE: Bone identification and segmentation in X-ray images are crucial in orthopedics for the automation of clinical procedures, but it often involves some manual operations. In this work, using a modified SegNet neural network, we automatically i...

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Medical physics
BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBC...

Path Generator with Unpaired Samples Employing Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these ...

Prior Attention Network for Multi-Lesion Segmentation in Medical Images.

IEEE transactions on medical imaging
The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However...

A Unified Deep Learning Framework for ssTEM Image Restoration.

IEEE transactions on medical imaging
Serial section transmission electron micro-scopy (ssTEM) reveals biological information at a scale of nanometer and plays an important role in the ultrastructural analysis. However, due to the imperfect preparation of biological samples, ssTEM images...

Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction.

IEEE transactions on medical imaging
Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morpho...

BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets.

Journal of molecular biology
Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers' eyes to draw conclusions. Our Bioimage Analysi...

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image reconstruction from low-dose tomographic data is an active research field, recently revolutionized by the advent of deep learning. In fact, deep learning typically yields superior results than classical optimization approaches, but unst...