Fully Automatic Segmentation of Anatomy and Scar from LGE-MRI
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Abstract:
The leading cause of death worldwide are cardiovascular diseases. In addition, the
number of patients suffering from heart failure is rising. The underlying cause of heart failure is often a myocardial infarction. For diagnosis in clinical routine, cardiac magnetic resonance imaging is used, as it provides information about morphology, blood flow, perfusion, and tissue characterization. In more detail, the analysis of the
tissue viability is very important for diagnosis, procedure planning, and guidance, i.e., for implantation of a bi-ventricular pacemaker. The clinical gold standard for
the viability assessment is 2-D late gadolinium enhanced magnetic resonance imaging
(LGE-MRI). In the last years, the imaging quality continuously improved and LGE-MRI was extended to a 3-D whole heart scan. This scan guarantees an accurate
quantification of the myocardium to the extent of myocardial scarring.
The main challenge arises in the accurate segmentation and analysis of such images. In this work, novel methods for the segmentation of the LGE-MRI data sets, both 2-D and 3-D, are proposed. One important goal is the direct segmentation of the LGE-MRI and the independence of an anatomical scan to avoid errors from the anatomical scan contour propagation. For the 2-D LGE-MRI segmentation, the short
axis stack of the left ventricle (LV) is used. First, the blood pool is detected and a rough outline is maintained by a morphological active contours without edges approach. Afterwards, the endocardial and epicardial boundary is estimated by either a filter or learning based method in combination with a minimal cost path search in polar space. For the endocardial contour refinement, an additional scar exclusion step is added. For the 3-D LGE-MRI, the LV is detected within the whole heart scan. In the next step, the short axis view is estimated using principal component analysis. For the endocardial and epicardial boundary estimation also a filter based or learning
based approach can be applied in combination with dynamic programming in polar
space. Furthermore, because of the high resolution also the papillary muscles are
segmented.
In addition to the fully automatic LV segmentation approaches, a generic semi-
automatic method based on Hermite radial basis function interpolation is introduced in combination with a smart brush. Effective interactions with less number of equations accelerate the performance and therefore, a real-time and an intuitive, interactive segmentation of 3-D objects is supported effectively.
After the segmentation of the left ventricle’s myocardium, the scar tissue is quantified. In this thesis, three approaches are investigated. The full-width-at-half-max algorithm and the x-standard deviation methods are implemented in a fully automatic manner. Furthermore, a texture based scar classification algorithm is introduced.
Subsequently, the scar tissue can be visualized, either in 3-D as a surface mesh or in 2-D projected onto the 16 segment bull’s eye plot of the American Heart Association.
However, for precise procedure planning and guidance, the information about the scar transmurality is very important. Hence, a novel scar layer visualization is introduced.
Therefore, the scar tissue is divided into three layers depending on the location of
the scar within the myocardium. With this novel visualization, an easy distinction between endocardial, mid-myocardial, or epicardial scar is possible. The scar layers
can also be visualized in 3-D as surface meshes or in 2-D projected onto the 16
segment bull’s eye plot.Cite
We proposed a new method for computer-based segmentation of myocardium tissue of left ventricular in series of full short axis MRI. First we locate the cardiac region in full short axis MRI data and then left ventricular cavity is searched and segmented in that region. Using this new approach, great result has been obtained in determination of left ventricular cavity. After finding the left ventricular cavity, the myocardium tissue is separated from other parts using a dilating circle starting from the left ventricular cavity. Separating myocardium tissue in short axis MRI data may lead to a very good analytic tool for interpretation of heart problems and analyzing left ventricular. In addition to preciseness, the proposed method is fully adaptive and has no dependence on a particular image. Beside it is deploys computational effective algorithm which results in an easy and direct implementation using a simple DSP hardware.
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Catheter ablation is an important option to treat ventricular tachycardias (VT). Scar-related VT is among the most difficult to treat, because myocardial scar, which is the underlying arrhythmogenic substrate, is patient-specific and often highly complex. The scar image from preprocedural late gadolinium enhancement magnetic resonance imaging (LGE- MRI) can provide high-resolution substrate information and, if integrated at the early stage of the procedure, can largely facilitate the procedure with image guidance. In clinical practice, however, early MRI integration is difficult because available integration tools rely on matching the MRI surface mesh and electroanatomical mapping (EAM) points, which is only possible after extensive EAM has been performed. In this paper, we propose to use a priori information on patient posture and a multi-sequence MRI integration framework to achieve accurate MRI integration that can be accomplished at an early stage of the procedure. From the MRI sequences, the left ventricular (LV) geometry, myocardial scar characteristics, and an anatomical landmark indicating the origin of the left main coronary artery are obtained preprocedurally using image processing techniques. Thereby the integration can be realized at the beginning of the procedure after acquiring a single mapping point. The integration method has been evaluated postprocedurally in terms of LV shape match and actual scar match. Compared to the iterative closest point (ICP) method that uses high-intensity mapping (225±49 points), our method using one mapping point reached a mean point-to-surface distance of 5.09±1.09 mm (vs. 3.85±0.60 mm, p<0.05), and scar correlation of -0.51±0.14 (vs. -0.50±0.14, p=NS).
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For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose an approach for fully automatic LV segmentation in 2-D LGE-MRI. First, the LV is automatically detected using circular Hough transforms. Second, the blood pool is approximated by applying a morphological active contours approach. The refinement of the endo- and epicardial contours is performed in polar space, considering the edge information and scar distribution. The proposed method was evaluated on 26 clinical LGE-MRI data sets. This comparison resulted in a Dice coefficient of 0.85 ± 0.06 for the endocardium and 0.84 ± 0.06 for the epicardium.
Endocardium
Sørensen–Dice coefficient
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Ischaemic heart disease is the number one cause of death world wide, which is in close relation with heart failure. If patients suffer from drug-refractory heart failure with a reduced ejection fraction, cardiac resynchronization therapy is a treatment option. For planning the procedure, precise information about the left ventricle's anatomy and scar distribution is required. The clinical gold standard to visualize scar is late gadolinium enhanced magnetic resonance imaging (LGE-MRI). The challenge arises in the myocardium segmentation of these sequences which is a pre-requisite for an accurate scar quantification. In this work, we compare a filter based approach against a learning based approach for LGE-MRI segmentation. For both approaches the segmentation workflow consists of four major steps. First, the left ventricle is detected. Second, the blood pool is estimated. Third, the endocardium is refined using scar information. Fourth, the epicardium is extracted.The proposed methods were evaluated on 100 clinical LGE-MRI data sets. For the learning based approach a 5-fold nested cross-validation is applied to evaluate the hyper-parameters. The learning based segmentation achieves slightly better results, with a Dice score of 0.82 ± 0.09 for the endocard and 0.81 ± 0.08 for the epicard.
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Purpose Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three‐dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four‐dimensional, 4D), remains challenging. This study proposes a deformable‐based segmentation methodology for efficiently segmenting 4D (3D + t) cardiac MR images. Methods The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set‐based shape prior method was applied to generate the LV epicardial contours and the RV boundary. Results This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62 ± 5.47%, 87.35 ± 7.26%, and 82.63 ± 6.22% for the LV endocardial, LV epicardial, and RV contours, respectively. Conclusions We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.
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Ground truth
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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.
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Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important tool that used for evaluating cardiac function by calculating different cardiac parameters. The manual segmentation of the left ventricle in all image sequences takes a lot of time, and therefore the automatic segmentation of the left ventricle is main step in cardiac function evaluation. In this paper, we proposed an automatic method for segmenting the left ventricle in cardiac MRI images. We applied pixel classification method by using number of features and KNN classifier for segmenting the left ventricle Cavity, and from its output we can get the endocardial contour. Then, we transformed image pixels from Cartesian to polar coordinates for segmenting the epicardial contour. This method was tested on large number of images, and we achieved good results reached to 95.61% sensitivity, and 98.9% specificity for endocardium segmentation, and 93.32% sensitivity, and 98.49% specificity for epicardium segmentation. The results of the proposed method show the availability for fast and reliable segmentation of the left ventricle.
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In medical diagnosis, the movement of the myocardium of left ventricle (LV) can represent the pump function of the heart, which can provide the basis for diagnosis of heart diseases. Magnetic resonance imaging (MRI) is an effective tool for the clinical diagnosis of heart diseases due to its special imaging mechanism, which is particularly effective for soft tissue such as heart. Identification of the LV endocardium, especially the apical and basal slice images and some special mid-ventricular slice images with poor image quality, is still a very challenging and open problem. In this paper, an automatic segmentation method based on threshold is proposed. This method works well in some mid-ventricular slices with poor image quality and some ventricle slices with messy edge. We tested the proposed method with other 15 popular segmentation algorithms by ten frames of Cardiac MRI at end systole (ED) or end diastole (ES) phases. Those frames of images are difficult to segment the LV endocardium from Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 challenge. Those frames of images are from apical and basal slices or mid-ventricular slices that are difficult to segment the LV endocardium. Finally, we assessed the deviation between the automatically segmented and benchmark manual contours. The proposed method achieved 0.9384 average Dice metric, 1.2715 mm average perpendicular distance (APD). These results compared with other algorithms demonstrate that the proposed method is an effective and viable method to identify the LV endocardium at ED and ES phases.
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