Cristobal Arrieta1,2, Sergio Uribe2,3, Vicente Parot1,2, Pablo Irarrazaval1,2, Carlos Sing-Long2, Cristian Tejos1,2
1Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, RM, Chile; 2Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, RM, Chile; 3Department of Radiology, Pontificia Universidad Catolica de Chile, Santiago, RM, Chile
Cardiac performance is typically evaluated from left ventricle volume estimations obtained by segmenting cardiac MRI data. This segmentation is usually done by a tedious manual process, that shows low reproducibility. Different authors have proposed the use of Level-Sets to automate this process. Due to the presence of multiple objects in the image, Level-Sets normally require shape constrains that are built from a training procedures. Training-based segmentations tend to fail with severely abnormal anatomies. We propose the use of Level-Sets with Preserved Topology. Compared to manual segmentations we achieved equally accurate and more reproducible segmentations without training, even in abnormal patients.