Combining 3D Deformable Models and Level Set Methods for the Segmentation of Abdominal Aortic Aneurysms

Abstract

In this paper we present a system that combines the benefits of 3D deformable models and level set methods for medical volume segmentation. Our 3D deformable model is a very computationally efficient method for segmenting medical volumes, however it is not currently able to segment features, such as renal arteries, that are small relative to the imaging slice thickness used. Level Set methods are an alternative approach to deformable models that re-pose the volume segmentation problem as the calculation of the steady state of an initial value Partial Differential Equation (PDE) system on a regular rectilinear or cubic mesh. The segmentation obtained is parameterised by the zero value level set of this mesh (analogous to an iso-surface). These methods are very computationally expensive, but have the advantage of being able to segment relatively small features such as renal arteries. The problem domain explored in this paper is the segmentation of arterial structures. The results of these segmentations are to be used in the assessment of patient suitability for minimally invasive (keyhole) surgical procedures in patients with abnormal aortic aneurysms. An abdominal aortic aneurysm (AAA) is a dilation of the abdominal aorta. AAAs usually increase in size with time, and if left untreated eventually rupture causing catastrophic haemorrhage. An AAA may be treated by conventional surgical methods, but increasingly minimally invasive techniques, where a stent graft is placed in the lumen, are being used. Patient suitability is assessed using CT data and a calibrated projection angiogram - only about 10% of patients are suitable for the keyhole repair. Once a candidate has been assessed as suitable, measurements are made from the same images to determine the key dimensions of the required stent. Our overall aim is to automate both of these stages of image analysis, ensuring that the full 3D nature of the CT is used. In this paper we describe a segmentation aproach that combines the benefits of a 3D deformable model