title: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework.
abstract: Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels. It is, for instance, important to extract the left atrium and the proximal part of the pulmonary veins to support guidance of ablation procedures for atrial fibrillation treatment. For cardiac resynchronization therapy, a heart model including the coronary sinus is needed. We present a heart model comprising the four heart chambers and the attached great vessels. By assigning individual linear transformations to the heart chambers and to short tubular segments building the great vessels, variable sizes of the heart chambers and bending of the vessels can be described in a consistent way. A configurable algorithmic framework that we call adaptation engine matches the heart model automatically to cardiac CT angiography images in a multi-stage process. First, the heart is detected using a Generalized Hough Transformation. Subsequently, the heart chambers are adapted. This stage uses parametric as well as deformable mesh adaptation techniques. In the final stage, segments of the large vascular structures are successively activated and adapted. To optimize the computational performance, the adaptation engine can vary the mesh resolution and freeze already adapted mesh parts. The data used for validation were independent from the data used for model-building. Ground truth segmentations were generated for 37 CT data sets reconstructed at several cardiac phases from 17 patients. Segmentation errors were assessed for anatomical sub-structures resulting in a mean surface-to-surface error ranging 0.50-0.82mm for the heart chambers and 0.60-1.32mm for the parts of the great vessels visible in the images.
authors: Philips Research Europe - Aachen, X-ray Imaging, 52062 Aachen, Germany.
authors: Olivier
authors: O
authors: Ecabert
authors: Jochen
authors: J
authors: Peters
authors: Matthew J
authors: MJ
authors: Walker
authors: Thomas
authors: T
authors: Ivanc
authors: Cristian
authors: C
authors: Lorenz
authors: Jens
authors: J
authors: von Berg
authors: Jonathan
authors: J
authors: Lessick
authors: Mani
authors: M
authors: Vembar
authors: Jürgen
authors: J
authors: Weese
mesh_terms: D001013:Aorta, Thoracic / Q000000981:diagnostic imaging* / Q000528:radiation effects*; D003198:Computer Simulation; D054326:Coronary Sinus / Q000000981:diagnostic imaging; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D011651:Pulmonary Artery / Q000000981:diagnostic imaging*; D011667:Pulmonary Veins / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed; D014684:Venae Cavae / Q000000981:diagnostic imaging*