AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 2551 to 2560 of 5128 articles

Automated pancreas segmentation and volumetry using deep neural network on computed tomography.

Scientific reports
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation...

A deep learning framework for automated detection and quantitative assessment of liver trauma.

BMC medical imaging
BACKGROUND: Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitati...

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring.

Tomography (Ann Arbor, Mich.)
BACKGROUND: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strong...

Deep Supervised Domain Adaptation for Pneumonia Diagnosis From Chest X-Ray Images.

IEEE journal of biomedical and health informatics
Pneumonia is one of the most common treatable causes of death, and early diagnosis allows for early intervention. Automated diagnosis of pneumonia can therefore improve outcomes. However, it is challenging to develop high-performance deep learning mo...

Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.

International journal of medical sciences
Early detection of lung cancer is one way to improve outcomes. Improving the detection of nodules on chest CT scans is important. Previous artificial intelligence (AI) modules show rapid advantages, which improves the performance of detecting lung n...

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.

Computer methods and programs in biomedicine
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, thi...

Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment.

Clinical radiology
AIM: To develop a fully automated deep-learning-based approach to measure muscle area for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen without any case exclusion criteria, for opportunistic screening for frailty.

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.

The British journal of radiology
OBJECTIVE: To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR).