Tanja Kurzendorfer


Fully Automatic Segmentation of Anatomy and Scar from LGE-MRI


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.

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