AI Medical Compendium Topic

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

Whole Body Imaging

Showing 61 to 67 of 67 articles

Clear Filters

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

Medical physics
PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in hea...

Deep reconstruction model for dynamic PET images.

PloS one
Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the...

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.

Medical physics
PURPOSE: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional e...

Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect.

Studies in health technology and informatics
The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive ...

Automatic anatomy recognition in whole-body PET/CT images.

Medical physics
PURPOSE: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is...

3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold.

IEEE transactions on cybernetics
Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for act...

Bodypart Recognition Using Multi-stage Deep Learning.

Information processing in medical imaging : proceedings of the ... conference
Automatic medical image analysis systems often start from identifying the human body part contained in the image; Specifically, given a transversal slice, it is important to know which body part it comes from, namely "slice-based bodypart recognition...