8 research outputs found
Comparison of image processing techniques for nonviable tissue quantification in late gadolinium enhancement cardiac magnetic resonance images
Purpose: The aim of this study was to compare the performance of quantitative methods, either semiautomated or automated, for left ventricular (LV) nonviable tissue analysis from cardiac magnetic resonance late gadolinium enhancement (CMR-LGE) images. Materials and Methods: The investigated segmentation techniques were: (i) n-standard deviations thresholding; (ii) full width at half maximum thresholding; (iii) Gaussian mixture model classification; and (iv) fuzzy c-means clustering. These algorithms were applied either in each short axis slice (single-slice approach) or globally considering the entire short-axis stack covering the LV (global approach). CMR-LGE images from 20 patients with ischemic cardiomyopathy were retrospectively selected, and results from each technique were assessed against manual tracing. Results: All methods provided comparable performance in terms of accuracy in scar detection, computation of local transmurality, and high correlation in scar mass compared with the manual technique. In general, no significant difference between single-slice and global approach was noted. The reproducibility of manual and investigated techniques was confirmed in all cases with slightly lower results for the nSD approach. Conclusions: Automated techniques resulted in accurate and reproducible evaluation of LV scars from CMR-LGE in ischemic patients with performance similar to the manual technique. Their application could minimize user interaction and computational time, even when compared with semiautomated approaches
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
A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images
10.1109/TBME.2013.2237907IEEE Transactions on Biomedical Engineering6061499-1508IEBE
A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images
Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly
visualize nonviable myocardium with hyperenhanced intensities with respect to
normal myocardium. For heart attack patients, it is crucial to facilitate the
decision of appropriate therapy by analyzing and quantifying their LGE CMR
images. To achieve accurate quantification, LGE CMR images need to be processed
in two steps: segmentation of the myocardium followed by classification of
infarcts within the segmented myocardium. However, automatic segmentation is
difficult usually due to the intensity heterogeneity of the myocardium and
intensity similarity between the infarcts and blood pool. Besides, the slices
of an LGE CMR dataset often suffer from spatial and intensity distortions,
causing further difficulties in segmentation and classification. In this paper,
we present a comprehensive 3-D framework for automatic quantification of LGE
CMR images. In this framework, myocardium is segmented with a novel method that
deforms coupled endocardial and epicardial meshes and combines information in
both short- and long-axis slices, while infarcts are classified with a
graph-cut algorithm incorporating intensity and spatial information. Moreover,
both spatial and intensity distortions are effectively corrected with specially
designed countermeasures. Experiments with 20 sets of real patient data show
visually good segmentation and classification results that are quantitatively
in strong agreement with those manually obtained by experts.Comment: IEEE Transactions on Biomedical Engineering ( Volume: 60, Issue: 6,
June 2013
Automatic segmentation in CMR - Development and validation of algorithms for left ventricular function, myocardium at risk and myocardial infarction
In this thesis four new algorithms are presented for automatic segmentation in cardiovascular magnetic resonance (CMR); automatic segmentation of the left ventricle, myocardial infarction, and myocardium at risk in two different image types. All four algorithms were implemented in freely available software for image analysis and were validated against reference delineations with a low bias and high regional agreement. CMR is the most accurate and reproducible method for assessment of left ventricular mass and volumes and reference standard for assessment of myocardial infarction. CMR is also validated against single photon emission computed tomography (SPECT) for assessment of myocardium at risk up to one week after acute myocardial infarction. However, the clinical standard for quantification of left ventricular mass and volumes is manual delineation which has been shown to have a large bias between observers from different sites and for myocardium at risk and myocardial infarction there is no clinical standard due to varying results shown for the previously suggested threshold methods. The new automatic algorithms were all based on intensity classification by Expectation Maximization (EM) and incorporation of a priori information specific for each application. Validation was performed in large cohorts of patients with regards to bias in clinical parameters and regional agreement as Dice Similarity Coefficient (DSC). Further, images with reference delineation of the left ventricle were made available for future benchmarking of left ventricular segmentation, and the new automatic algorithms for segmentation of myocardium at risk and myocardial infarction were directly compared to the previously suggested intensity threshold methods. Combining intensity classification by EM with a priori information as in the new automatic algorithms was shown superior to previous methods and specifically to the previously suggested threshold methods for myocardium at risk and myocardial infarction. Added value of using a priori information and intensity correction was shown significant measured by DSC even though not significant for bias. For the previously suggested methods of infarct quantification a poorer result was found in the new multi-center, multi-vendor patient data than in the original validation in animal studies or single center patient studies. Thus, the results in this thesis also show the importance ofusing both bias and DSC for validation and performing validation in images of representative quality as in multi-center, multi-vendor patient studies
IEEE Transactions On Biomedical Engineering : Vol. 60, No. 6, June 2013
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etc