Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model
Dominik K. Fritz, Daniel Rinck, Roland Unterhinninghofen, Ruediger Dillmann, Michael Scheuering · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
The manual segmentation and analysis of high-resolution multi-slice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium and compute the relevant diagnostic parameters. In this work we present a semi-automatic cardiac segmentation approach with minimal user interaction. It is based on a combination of an adaptive slice-based regiongrowing and a modified Active Shape Model (ASM). Starting with a single manual click point in the ascending aorta, the aorta, the left atrium and the left ventricle get segmented with the slice-based adaptive regiongrowing. The approximate position of the aortic and mitral valve as well as the principal axes of the left ventricle (LV) are determined. To prevent the regiongrowing from draining into neighboring anatomical structures via CT artifacts, we implemented a draining control by examining a cubic region around the currently processed voxel. Additionally, we use moment-based parameters to integrate simple anatomical knowledge into the regiongrowing process. Using the results of the preceding regiongrowing process, a ventricle-centric and normalized coordinate system is established which is used to adapt a previously trained ASM to the image, using an iterative multi-resolution approach. After fitting the ASM to the image, we can use the generated model-points to create an exact surface model of the left ventricular myocardium for visualization and for computing the diagnostically relevant parameters, like the ventricular blood volume and the myocardial wall thickness.