8,348 research outputs found
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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
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
A coupled mitral valve -- left ventricle model with fluid-structure interaction
Understanding the interaction between the valves and walls of the heart is
important in assessing and subsequently treating heart dysfunction. With
advancements in cardiac imaging, nonlinear mechanics and computational
techniques, it is now possible to explore the mechanics of valve-heart
interactions using anatomically and physiologically realistic models. This
study presents an integrated model of the mitral valve (MV) coupled to the left
ventricle (LV), with the geometry derived from in vivo clinical magnetic
resonance images. Numerical simulations using this coupled MV-LV model are
developed using an immersed boundary/finite element method. The model
incorporates detailed valvular features, left ventricular contraction,
nonlinear soft tissue mechanics, and fluid-mediated interactions between the MV
and LV wall. We use the model to simulate the cardiac function from diastole to
systole, and investigate how myocardial active relaxation function affects the
LV pump function. The results of the new model agree with in vivo measurements,
and demonstrate that the diastolic filling pressure increases significantly
with impaired myocardial active relaxation to maintain the normal cardiac
output. The coupled model has the potential to advance fundamental knowledge of
mechanisms underlying MV-LV interaction, and help in risk stratification and
optimization of therapies for heart diseases.Comment: 25 pages, 6 figure
Modelling mitral valvular dynamics–current trend and future directions
Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed
Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction
In this research, we hypothesized that novel biomechanical parameters are discriminative in patients following acute ST-segment elevation myocardial infarction (STEMI). To identify these biomechanical biomarkers and bring computational biomechanics ‘closer to the clinic’, we applied state-of-the-art multiphysics cardiac modelling combined with advanced machine learning and multivariate statistical inference to a clinical database of myocardial infarction. We obtained data from 11 STEMI patients (ClinicalTrials.gov NCT01717573) and 27 healthy volunteers, and developed personalized mathematical models for the left ventricle (LV) using an immersed boundary method. Subject-specific constitutive parameters were achieved by matching to clinical measurements. We have shown, for the first time, that compared with healthy controls, patients with STEMI exhibited increased LV wall active tension when normalized by systolic blood pressure, which suggests an increased demand on the contractile reserve of remote functional myocardium. The statistical analysis reveals that the required patient-specific contractility, normalized active tension and the systolic myofilament kinematics have the strongest explanatory power for identifying the myocardial function changes post-MI. We further observed a strong correlation between two biomarkers and the changes in LV ejection fraction at six months from baseline (the required contractility (r = − 0.79, p < 0.01) and the systolic myofilament kinematics (r = 0.70, p = 0.02)). The clinical and prognostic significance of these biomechanical parameters merits further scrutinization
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Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging.
In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R2 = 0.93, P < 0.001) but 3-slice TPD showed a small yet significant bias compared to whole-heart TPD (- 1.19%; P < 0.0001) and the 95% limits of agreement were relatively wide (- 6.65% to 4.27%). Incomplete ventricular coverage typically acquired in 3-slice CMR MPI does not significantly affect the diagnostic accuracy. However, 3-slice MPI may fail to detect severe apical ischemia and underestimate the extent/severity of perfusion defects. Our results suggest that caution is required when comparing the ischemic burden between 3-slice and whole-heart datasets, and corroborate the need to establish prognostic thresholds specific to each approach
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN
Anatomical and biophysical modeling of left atrium (LA) and proximal
pulmonary veins (PPVs) is important for clinical management of several cardiac
diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
and PPVs through visualization. However, there is a strong need for an advanced
image segmentation method to be applied to cardiac MRI for quantitative
analysis of LA and PPVs. In this study, we address this unmet clinical need by
exploring a new deep learning-based segmentation strategy for quantification of
LA and PPVs with high accuracy and heightened efficiency. Our approach is based
on a multi-view convolutional neural network (CNN) with an adaptive fusion
strategy and a new loss function that allows fast and more accurate convergence
of the backpropagation based optimization. After training our network from
scratch by using more than 60K 2D MRI images (slices), we have evaluated our
segmentation strategy to the STACOM 2013 cardiac segmentation challenge
benchmark. Qualitative and quantitative evaluations, obtained from the
segmentation challenge, indicate that the proposed method achieved the
state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and
efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).Comment: The paper is accepted by MICCAI 2017 for publicatio
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes
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