1,384 research outputs found
Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR
<p>Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.</p>
<p>Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.</p>
<p>Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.</p>
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation
Analysis and modeling of the ventricles and myocardium are important in the
diagnostic and treatment of heart diseases. Manual delineation of those tissues
in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the
boundaries makes the segmentation task rather challenging. Furthermore, the
annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,
are often not available. We propose an end-to-end segmentation framework based
on convolutional neural network (CNN) and adversarial learning. A dilated
residual U-shape network is used as a segmentor to generate the prediction
mask; meanwhile, a CNN is utilized as a discriminator model to judge the
segmentation quality. To leverage the available annotations across modalities
per patient, a new loss function named weak domain-transfer loss is introduced
to the pipeline. The proposed model is evaluated on the public dataset released
by the challenge organizer in MICCAI 2019, which consists of 45 sets of
multi-sequence CMR images. We demonstrate that the proposed adversarial
pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
© Springer Nature Switzerland AG 2020. Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences
Automatic segmentation of the left ventricle cavity and myocardium in MRI data
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method
Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI)
is the gold standard technique for myocardial viability assessment. Although
the technique accurately reflects the damaged tissue, there is no clinical
standard for quantifying myocardial infarction (MI), demanding most algorithms
to be expert dependent. Objectives and Methods: In this work a new automatic
method for MI quantification from LGE-MRI is proposed. Our novel segmentation
approach is devised for accurately detecting not only hyper-enhanced lesions,
but also microvascular-obstructed areas. Moreover, it includes a myocardial
disease detection step which extends the algorithm for working under healthy
scans. The method is based on a cascade approach where firstly, diseased slices
are identified by a convolutional neural network (CNN). Secondly, by means of
morphological operations a fast coarse scar segmentation is obtained. Thirdly,
the segmentation is refined by a boundary-voxel reclassification strategy using
an ensemble of CNNs. For its validation, reproducibility and further comparison
against other methods, we tested the method on a big multi-field expert
annotated LGE-MRI database including healthy and diseased cases. Results and
Conclusion: In an exhaustive comparison against nine reference algorithms, the
proposal achieved state-of-the-art segmentation performances and showed to be
the only method agreeing in volumetric scar quantification with the expert
delineations. Moreover, the method was able to reproduce the intra- and
inter-observer variability ranges. It is concluded that the method could
suitably be transferred to clinical scenarios.Comment: Submitted to IEE
Recommended from our members
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
Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI
Automated extraction of quantitative parameters from Cardiac Magnetic
Resonance Images (CMRI) is crucial for the management of patients with
myocardial infarct. This work proposes a post-processing procedure to jointly
analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an
automatic quantification of myocardial contraction and enhancement parameters
and a study of their relationship. For that purpose, the following processes
are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D
DE/Cine rigid registration in a region about the heart, 3) segmentation of the
myocardium on Cine MRI and superimposition of the epicardial and endocardial
contours on the DE images, 4) quantification of the Myocardial Infarct Extent
(MIE), 5) study of the regional contractile function using a new index, the
Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients
with clinically proven myocardial infarction. The comparison between the MIE
and the visually assessed regional function scores demonstrated that the MIE is
highly related to the severity of the wall motion abnormality. In addition, it
was shown that the newly developed regional myocardial contraction parameter
(ATR) decreases significantly in delayed enhanced regions. This largely
automated approach enables a combined study of regional MIE and left
ventricular function
- …