AIMC Topic: Image Processing, Computer-Assisted

Clear Filters Showing 2461 to 2470 of 10288 articles

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.

Medical physics
BACKGROUND: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is ineffic...

Advancing laser ablation assessment in hyperspectral imaging through machine learning.

Computers in biology and medicine
Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimens...

Finite element models with automatic computed tomography bone segmentation for failure load computation.

Scientific reports
Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedi...

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Medical physics
BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray ...

Current status and future directions in artificial intelligence for nuclear cardiology.

Expert review of cardiovascular therapy
INTRODUCTION: Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction are critical for high-quality imaging, but this can be technically challeng...

Convolutional neural network for automated tooth segmentation on intraoral scans.

BMC oral health
BACKGROUND: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propo...

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.

Scientific reports
Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep le...

Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing....

A deep learning anthropomorphic model observer for a detection task in PET.

Medical physics
BACKGROUND: Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool f...

Unsupervised shape-and-texture-based generative adversarial tuning of pre-trained networks for carotid segmentation from 3D ultrasound images.

Medical physics
BACKGROUND: Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensitive to the change of carotid atherosclerosis in response to medical/dietary interventions. Manual segmentation of the media-adventitia boundary (MAB) a...