AIMC Topic: Imaging, Three-Dimensional

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Q&A: Understanding the composition of behavior.

BMC biology
Understanding the brain requires understanding behavior. New machine vision and learning techniques are poised to revolutionize our ability to analyze behaviors exhibited by animals in the laboratory. Here we describe one such method, Motion Sequenci...

Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation.

Medical image analysis
Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These st...

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.

NeuroImage. Clinical
Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathog...

MCRDR Knowledge-Based 3D Dialogue Simulation in Clinical Training and Assessment.

Journal of medical systems
Dialogue-based simulation is a real-world practice technique for medical and clinical education that provides students with an opportunity to train using hands-on experiences without putting actual patients being put at risk. In this paper, a 3D inte...

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

Magnetic resonance in medicine
PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.

Nature medicine
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US scree...

Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging.

IEEE transactions on medical imaging
The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accura...

Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) is an indispensable tool in diagnosing brain-tumor patients. Automated tumor segmentation is being widely researched to accelerate the MRI analysis and allow clinicians to precisely plan trea...

Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans.

Sensors (Basel, Switzerland)
Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radi...

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

PloS one
Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a ...