Automatic model-based segmentation of the heart in CT images.

Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations.

Assessment of automatic vessel tracking techniques in preoperative planning of transluminal aortic stent graft implantation.

Objective: To evaluate automatic vessel tracking techniques in the course of preoperative planning prior to transluminal aortic endograft implantation by comparing accuracy, reproducibility, and postprocessing time with source image and volume-rendered analysis methods.

Methods: Multislice computed tomography datasets of 5 patients with abdominal aortic aneurysms were preoperatively examined, performing volumetric analysis of diameter and position of renal artery orifices, aneurysmal neck, maximal aneurysmal extension, aortic bifurcation, and iliac arteries and bifurcation. Analysis was realized by utilizing transverse datasets, volume rendering, and automated vessel tracking strategies (MxView, Philips, Best, The Netherlands).