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Domain generalization in deep learning for contrast-enhanced imaging.

Computers in biology and medicine
BACKGROUND: The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contra...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Scientific reports
Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially hi...

A learning-based, region of interest-tracking algorithm for catheter detection in echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Echocardiography (echo) is gaining popularity to guide the catheter during surgical procedures. However, it is difficult to discern the catheter tip in echo even with an acoustically active catheter. An acoustically active catheter is detected for th...

Great debates in cardiac computed tomography: OPINION: "Artificial intelligence and the future of cardiovascular CT - Managing expectation and challenging hype".

Journal of cardiovascular computed tomography
This manuscript has been written as a follow-up to the "AI/ML great debate" featured at the 2021 Society of Cardiovascular Computed Tomography (SCCT) Annual Scientific Meeting. In debate style, we highlighti the need for expectation management of AI/...

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Sensors (Basel, Switzerland)
The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to es...

Cardiac MRI segmentation with sparse annotations: Ensembling deep learning uncertainty and shape priors.

Medical image analysis
The performance of deep learning for cardiac magnetic resonance imaging (MRI) segmentation is oftentimes degraded when using small datasets and sparse annotations for training or adapting a pre-trained model to previously unseen datasets. Here, we de...

Impact of deep learning architectures on accelerated cardiac T mapping using MyoMapNet.

NMR in biomedicine
The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T estimation using accelerated cardiac T mapping from four T -weighted images collected after a single inv...

Compact pediatric cardiac magnetic resonance imaging protocols.

Pediatric radiology
Cardiac MRI is in many respects an ideal modality for pediatric cardiovascular imaging, enabling a complete noninvasive assessment of anatomy, morphology, function and flow in one radiation-free and potentially non-contrast exam. Nonetheless, traditi...

End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventri...

Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.

Scientific reports
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical con...