147 research outputs found
Effectiveness of Visualisations for Detection of Errors in Segmentation of Blood Vessels
Vascular disease diagnosis often requires a precise segmentation of the vessel lumen. When 3D (Magnetic Resonance Angiography, MRA, or Computed Tomography Angiography, CTA) imaging is available, this can be done automatically, but occasional errors are inevitable. So, the segmentation has to be checked by clinicians. This requires appropriate visualisation techniques. A number of visualisation techniques exist, but there has been little in the way of user studies that compare the different alternatives. In this study we examine how users interact with several basic visualisations, when performing a visual search task, checking vascular segmentation correctness of segmented MRA data. These visualisations are: direct volume rendering (DVR), isosurface rendering, and curved planar reformatting (CPR). Additionally, we examine if visual highlighting of potential errors can help the user find errors, so a fourth visualisation we examine is DVR with visual highlighting. Our main findings are that CPR performs fastest but has higher error rate, and there are no significant differences between the other three visualisations. We did find that visual highlighting actually has slower performance in early trials, suggesting that users learned to ignore them
Computer-aided detection of wall motion abnormalities in cardiac MRI
With the increasing prevalence and hospitalization rate of ischaemic heart disease, an explosive growth of diagnostic imaging for ischaemia is ongoing. Clinical decision making on revascularization procedures requires reliable viability assessment to assure long-term patient survival and to elevate cost effectiveness of the therapy and treatment. As such, the demand is increasing for a computer-assisted diagnosis (CAD) method for ischaemic heart disease that supports clinicians with an objective analysis of infarct severity, a viability assessment or a prediction of potential functional improvement before performing revascularization. The goal of this thesis was to explore novel mechanisms that can be used for CAD in ischaemic heart disease, particularly through wall motion analysis from cardiac MR images. Existing diagnostic treatment of wall motion analysis from cardiac MR relies on visual wall motion scoring, which suffers from inter- and intra-observer variability. To minimize this variability, the automated method must contain essential knowledge on how the heart contracts normally. This enables automatic quantification of regional abnormal wall motion, detection of segments with contractile reserve and prediction of functional improvement in stress.1. Bontius Stichting inz. Doelfonds beeldverwerking, 2. Foundation Imago, 3. ASCI research school, and 4. Library of the University of Leiden.UBL - phd migration 201
FastVentricle: Cardiac Segmentation with ENet
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contouring ventricular structures. Automatic contouring can lower the
required time per patient by generating contour suggestions that can be lightly
modified by the annotator. Fully convolutional networks (FCNs), a variant of
convolutional neural networks, have been used to rapidly advance the
state-of-the-art in automated segmentation, which makes FCNs a natural choice
for ventricular segmentation. However, FCNs are limited by their computational
cost, which increases the monetary cost and degrades the user experience of
production systems. To combat this shortcoming, we have developed the
FastVentricle architecture, an FCN architecture for ventricular segmentation
based on the recently developed ENet architecture. FastVentricle is 4x faster
and runs with 6x less memory than the previous state-of-the-art ventricular
segmentation architecture while still maintaining excellent clinical accuracy.Comment: 11 pages, 6 figures, Accepted to Functional Imaging and Modeling of
the Heart (FIMH) 201
4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics
4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6ā5.8% and 1.1ā3.8% in the phantom data and normal volunteer data, respectively
Atlas-based ventricular shape analysis for understanding congenital heart disease
Congenital heart disease is associated with abnormal ventricular shape that can affect wall mechanics and may be predictive of long-term adverse outcomes. Atlas-based parametric shape analysis was used to analyze ventricular geometries of eight adolescent or adult single-ventricle CHD patients with tricuspid atresia and Fontans. These patients were compared with an āatlasā of non-congenital asymptomatic volunteers, resulting in a set of Z-scores which quantify deviations from the control population distribution on a patient-by-patient basis. We examined the potential of these scores to: (1) quantify abnormalities of ventricular geometry in single ventricle physiologies relative to the normal population; (2) comprehensively quantify wall motion in CHD patients; and (3) identify possible relationships between ventricular shape and wall motion that may reflect underlying functional defects or remodeling in CHD patients. CHD ventricular geometries at end-diastole and end-systole were individually compared with statistical shape properties of an asymptomatic population from the Cardiac Atlas Project. Shape analysis-derived model properties, and myocardial wall motions between end-diastole and end-systole, were compared with physician observations of clinical functional parameters. Relationships between altered shape and altered function were evaluated via correlations between atlas-based shape and wall motion scores. Atlas-based shape analysis identified a diverse set of specific quantifiable abnormalities in ventricular geometry or myocardial wall motion in all subjects. Moreover, this initial cohort displayed significant relationships between specific shape abnormalities such as increased ventricular sphericity and functional defects in myocardial deformation, such as decreased long-axis wall motion. These findings suggest that atlas-based ventricular shape analysis may be a useful new tool in the management of patients with CHD who are at risk of impaired ventricular wall mechanics and chamber remodeling
Fully Automated Myocardial Strain Estimation from Cardiovascular MRIātagged Images Using a Deep Learning Framework in the UK Biobank
Purpose: To demonstrate the feasibility and performance of a fully automated
deep learning framework to estimate myocardial strain from short-axis cardiac
magnetic resonance tagged images. Methods and Materials: In this retrospective
cross-sectional study, 4508 cases from the UK Biobank were split randomly into
3244 training and 812 validation cases, and 452 test cases. Ground truth
myocardial landmarks were defined and tracked by manual initialization and
correction of deformable image registration using previously validated software
with five readers. The fully automatic framework consisted of 1) a
convolutional neural network (CNN) for localization, and 2) a combination of a
recurrent neural network (RNN) and a CNN to detect and track the myocardial
landmarks through the image sequence for each slice. Radial and circumferential
strain were then calculated from the motion of the landmarks and averaged on a
slice basis. Results: Within the test set, myocardial end-systolic
circumferential Green strain errors were -0.001 +/- 0.025, -0.001 +/- 0.021,
and 0.004 +/- 0.035 in basal, mid, and apical slices respectively (mean +/-
std. dev. of differences between predicted and manual strain). The framework
reproduced significant reductions in circumferential strain in diabetics,
hypertensives, and participants with previous heart attack. Typical processing
time was ~260 frames (~13 slices) per second on an NVIDIA Tesla K40 with 12GB
RAM, compared with 6-8 minutes per slice for the manual analysis. Conclusions:
The fully automated RNNCNN framework for analysis of myocardial strain enabled
unbiased strain evaluation in a high-throughput workflow, with similar ability
to distinguish impairment due to diabetes, hypertension, and previous heart
attack.Comment: accepted in Radiology Cardiothoracic Imagin
Orthogonal decomposition of left ventricular remodeling in myocardial infarction
Left ventricular size and shape are important for quantifying cardiac remodeling in response to cardiovascular disease. Geometric remodeling indices have been shown to have prognostic value in predicting adverse events in the clinical literature, but these often describe interrelated shape changes. We developed a novel method for deriving orthogonal remodeling components directly from any (moderately independent) set of clinical remodeling indices. Results: Six clinical remodeling indices (end-diastolic volume index, sphericity, relative wall thickness, ejection fraction, apical conicity, and longitudinal shortening) were evaluated using cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991 asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares (PLS) regression of left ventricular shape models resulted in remodeling components that were optimally associated with each remodeling index. A GramāSchmidt orthogonalization process, by which remodeling components were successively removed from the shape space in the order of shape variance explained, resulted in a set of orthonormal remodeling components. Remodeling scores could then be calculated that quantify the amount of each remodeling component present in each case. A one-factor PLS regression led to more decoupling between scores from the different remodeling components across the entire cohort, and zero correlation between clinical indices and subsequent scores. Conclusions: The PLS orthogonal remodeling components had similar power to describe differences between myocardial infarction patients and asymptomatic subjects as principal component analysis, but were better associated with well-understood clinical indices of cardiac remodeling. The data and analyses are available from www.cardiacatlas.org
Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank
Background
The associations between cardiovascular disease (CVD) risk factors and the biventricular geometry of the right ventricle (RV) and left ventricle (LV) have been difficult to assess, due to subtle and complex shape changes. We sought to quantify reference RV morphology as well as biventricular variations associated with common cardiovascular risk factors.
Methods
A biventricular shape atlas was automatically constructed using contours and landmarks from 4329 UK Biobank cardiovascular magnetic resonance (CMR) studies. A subdivision surface geometric mesh was customized to the contours using a diffeomorphic registration algorithm, with automatic correction of slice shifts due to differences in breath-hold position. A reference sub-cohort was identified consisting of 630 participants with no CVD risk factors. Morphometric scores were computed using linear regression to quantify shape variations associated with four risk factors (high cholesterol, high blood pressure, obesity and smoking) and three disease factors (diabetes, previous myocardial infarction and angina).
Results
The atlas construction led to an accurate representation of 3D shapes at end-diastole and end-systole, with acceptable fitting errors between surfaces and contours (average error less than 1.5āmm). Atlas shape features had stronger associations than traditional mass and volume measures for all factors (p <ā0.005 for each). High blood pressure was associated with outward displacement of the LV free walls, but inward displacement of the RV free wall and thickening of the septum. Smoking was associated with a rounder RV with inward displacement of the RV free wall and increased relative wall thickness.
Conclusion
Morphometric relationships between biventricular shape and cardiovascular risk factors in a large cohort show complex interactions between RV and LV morphology. These can be quantified by z-scores, which can be used to study the morphological correlates of disease
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
Cardiac left ventricle (LV) quantification provides a tool for diagnosing
cardiac diseases. Automatic calculation of all relevant LV indices from cardiac
MR images is an intricate task due to large variations among patients and
deformation during the cardiac cycle. Typical methods are based on segmentation
of the myocardium or direct regression from MR images. To consider cardiac
motion and deformation, recurrent neural networks and spatio-temporal
convolutional neural networks (CNNs) have been proposed. We study an approach
combining state-of-the-art models and emphasizing transfer learning to account
for the small dataset provided for the LVQuan19 challenge. We compare 2D
spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase
classification. To incorporate segmentation information, we propose an
architecture-independent segmentation-based regularization. To improve the
robustness further, we employ a search scheme that identifies the optimal
ensemble from a set of architecture variants. Evaluating on the LVQuan19
Challenge training dataset with 5-fold cross-validation, we achieve mean
absolute errors of 111 +- 76mm^2, 1.84 +- 0.9mm and 1.22 +- 0.6mm for area,
dimension and regional wall thickness regression, respectively. The error rate
for cardiac phase classification is 6.7%.Comment: Accepted at the MICCAI Workshop STACOM 201
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