The delineation of anatomical structures in medical images can be achieved in an efficient and robust manner using statistical anatomical organ models, which has been demonstrated for an already considerable set of organs, including the heart. While it is possible to provide models with sufficient shape variability to cope, to a large extent, with inter-patient variability, as long as object topology is conserved, it is a fundamental problem to cope with topological organ variability. We address this by creating a set of model variants and selecting the most appropriate model variant for the patient at hand. We propose a hybrid method combining model-based image analysis with a guided region growing approach for automated anatomical variant selection and apply it to the left atrium in cardiac CT images. Concerning the human heart, the left atrium is the most variable sub-structure with a variable number of pulmonary veins drainng into it. It is of large clinical interest in the context of atrial fibrillation and related interventions.
['Raghed Hanna', 'Hans Barschdorf', 'Tobias Klinder', 'F. Weber', 'Martin W. Krueger', 'Olaf Dössel', 'Cristian Lorenz']
A hybrid method for automatic anatomical variant detection and segmentation