3,331 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions
Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter
Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography
Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy
Quantitative imaging in cardiovascular CT angiography
In de afgelopen decennia is computertomografie (CT) een prominente niet-invasieve modaliteit om hart- en vaatziekten te evalueren geworden. Dit proefschrift heeft als doel de rol van CT in de therapeutische behandeling van coronaire hartziekte (CAD) en klepaandoeningen te onderzoeken.De relatie tussen kransslagadergeometrie (statisch en dynamisch) en aanwezigheid en omvang van CAD met CT werd onderzocht. De resultaten suggereren dat de statische geometrie van de kransslagader significant gerelateerd is aan de aanwezigheid van plaque en stenose. Er was echter geen verband tussen dynamische verandering van de coronaire arterie-geometrie en de ernst van CAD. Een algoritme om de invloed van intraluminair contrastmiddel op niet-verkalkte atherosclerotische plaque Hounsfield-Unit-waarden te corrigeren werd gepresenteerd en gevalideerd met behulp van fantomen.Diagnose en operatieplanning kunnen cruciale gevolgen hebben voor de klinische uitkomst van chirurgische ingrepen. In dit proefschrift wordt beschreven dat halfautomatische softwareprogramma’s het kwantificeren van het aortaklepgebied betere reproduceerbare resultaten toonden in vergelijking met handmatige metingen, en vergelijkbare resultaten met de huidige gouden standaard, de echocardiografie. Een systematische review over het dynamische gedrag van de aorta-annulus toont aan dat de vorm van de aorta-annulus tijdens de hartcyclus verandert, wat impliceert dat er bij het bepalen van een prothese rekening moet worden gehouden met meerdere fasen. Een andere review beschrijft het gebruik van 3D-printen in de chirurgische planning samen met andere toepassingen voor de behandeling van hartklepaandoeningen.CT is de belangrijkste beeldvormingsmodaliteit in deze onderzoeken, die gericht waren op de therapeutische behandeling van hart- en vaatziekten, van vroege risicobepaling tot diagnose en chirurgische planning.In the recent decades computed tomography (CT) has emerged as a dominant non-invasive modality to evaluate cardiovascular diseases. This thesis aimed to explore the role of CT in the therapeutic management of coronary artery disease (CAD) and valvular diseases.The relationship between both static and dynamic coronary artery geometry and presence and extent of CAD using CT was investigated. The results suggest that the static coronary artery geometry is significantly related to presence of plaque and significant stenosis. However, there were no such relationship between dynamic change of coronary artery geometry and severity of CAD. As part of this thesis an algorithm to correct the influence of lumen contrast enhancement on non-calcified atherosclerotic plaque Hounsfield-Unit values was presented. The algorithm was validated using phantoms. The diagnosis and surgical planning may have crucial impact on clinical outcome. Semi-automatic software for aortic valve area quantification presented in this thesis was proven to be more repeatable and similar to gold standard echocardiography in comparison to manual measurements. The systematic review regarding the dynamic behavior of aortic annulus revealed that aortic annulus geometry changes throughout the cardiac cycle which implies that multiple phases should be taken into account for prosthesis sizing. Another review in this thesis discusses the use of 3D printing in the surgical planning along with other applications for the treatment of valvular diseases.CT is the main imaging modality in these studies which were focused on the therapeutic management of cardiovascular diseases from early risk determination to diagnosis and surgical planning
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