AIMC Topic: Image Interpretation, Computer-Assisted

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Improving the detection of autism spectrum disorder by combining structural and functional MRI information.

NeuroImage. Clinical
Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase i...

Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification.

IEEE journal of biomedical and health informatics
Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represent...

Preliminary experience with an image-free handheld robot for total knee arthroplasty: 77 cases compared with a matched control group.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
BACKGROUND: Achieving an optimal limb alignment is an important factor affecting the long-term survival of total knee arthroplasty (TKA). This is the first study to look at the limb alignment and orientation of components in TKA using a novel image-f...

Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study.

NeuroImage. Clinical
The locus coeruleus (LC) has a long-established role in the attentional and arousal response to threat, and in the emergence of pathological anxiety in pre-clinical models. However, human evidence of links between LC function and pathological anxiety...

Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

European journal of radiology
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...

Machine learning derived input-function in a dynamic F-FDG PET study of mice.

Biomedical physics & engineering express
Tracer kinetic modelling, based on dynamic F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. O...

Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach.

Medical image analysis
Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (C...

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth.

Physics in medicine and biology
Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible ...

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks.

IEEE journal of biomedical and health informatics
For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and ...