Extracting the Fine Structure of the Left Cardiac Ventricle in 4D CT Data

Abstract

We propose a pipeline for the segmentation of the left cardiac ventricle (LV) in 4D CT data based on the random walker (RW) algorithm. A segmentation of the LV allows to extract clinical relevant parameters such as ejection fraction (EF) and volume over time (VoT), supporting diagnostic and therapy planning. The presented pipeline works aside approaches incorporating annotated databases, statistical shape modeling or atlas-based segmentation. We have tested our segmentation approach on six clinical 4D CT datasets including different pathologies and typical artifacts and compared the segmentation results to manually segmented slices. We achieve a minimum sensitivity of 86% and specificity of 96%. The resulting EF and VoT is comparable to known reference values and reflects the present pathologies correctly. Additionally, we tested three different routines for thresholding the RW probability maps. An interview with surgical and radiological experts together with high sensitivity scores indicates the superiority of the fixed threshold selection method – especially in the presence of pathologies. The segmentation is also correct near problematic fine structures such as cardiac valves, papillary muscles and the apex of the heart.