AIMC Topic: Tomography, X-Ray Computed

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Deep learning segmentation of major vessels in X-ray coronary angiography.

Scientific reports
X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable tr...

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.

Scientific reports
Labeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep learning models large amounts of data are needed. Standard data augmentation is a method to increase generalizability and is routinely performed. Generati...

Evaluation of an AI-based, automatic coronary artery calcium scoring software.

European radiology
OBJECTIVES: To evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

International journal of computer assisted radiology and surgery
PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malig...

Coronary artery calcium score quantification using a deep-learning algorithm.

Clinical radiology
AIM: To investigate the impact of a deep-learning algorithm on the quantification of coronary artery calcium score (CACS) and the stratification of cardiac risk.

Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features.

Journal of medical systems
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce th...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Medical image analysis
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, loc...

Novelties in imaging in pulmonary fibrosis and nodules. A narrative review.

Pulmonology
In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes....