AIMC Topic: Tomography, X-Ray Computed

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The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.

Journal of applied clinical medical physics
PURPOSE: Accurate segmentation of cardiac structures on coronary CT angiography (CCTA) images is crucial for the morphological analysis, measurement, and functional evaluation. In this study, we achieve accurate automatic segmentation of cardiac stru...

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Physics in medicine and biology
Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of...

Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.

European radiology
OBJECTIVES: To determine the diagnostic accuracy and image quality of ultra-low-dose computed tomography (ULDCT) with deep learning reconstruction (DLR) to evaluate patients with suspected urolithiasis, compared with ULDCT with hybrid iterative recon...

Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.

Human brain mapping
Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (...

Deep learning for automatic segmentation of paraspinal muscle on computed tomography.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Muscle quantification is an essential step in sarcopenia evaluation.

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mortality and morbidity. The fast and accurate detection of ICH is important for the patient to get an ...

A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis.

Computational intelligence and neuroscience
Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time t...

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Journal of healthcare engineering
Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVI...

A deep learning algorithm to improve readers' interpretation and speed of pancreatic cystic lesions on dual-phase enhanced CT.

Abdominal radiology (New York)
PURPOSE: To develop a deep learning model (DLM) to improve readers' interpretation and speed in the differentiation of pancreatic cystic lesions (PCLs) on dual-phase enhanced CT, and a low contrast media dose, external testing set validated the model...