Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis

Abstract

Automated and accurate segmentation of the aorta in 4D (3D+time) cardiovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissections. A computer-aided diagnosis method is reported that allows one to objectively identify subjects with connective tissue disorders from sixteen-phase 4D aortic MR images. Starting with a step of multi-view image registration, our automated segmentation method combines level-set and optimal surface segmentation algorithms in a single optimization process so that the final aortic surfaces in all 16 cardiac phases are determined. The resulting aortic lumen surface is registered with an aortic model followed by calculation of modal indices of aortic shape and motion. The modal indices reflect the differences of any individual aortic shape and motion from an average aortic behavior. A Support Vector Machine (SVM) classifier is used for the discrimination between normal and connective tissue disorder subjects. 4D MR image data sets acquired from 104 normal and connective tissue disorder MR datasets were used for development and performance evaluation of our method. The automated 4D segmentation resulted in accurate aortic surfaces in all 16 cardiac phases, covering the aorta from the aortic annulus to the diaphragm, yielding subvoxel accuracy with signed surface positioning errors of −0.07 ± 1.16 voxel (−0.10 ± 2.05 mm). The computer aided diagnosis method distinguished between normal and connective tissue disorder subjects with a classification correctness of 90.4%.

Minimally interactive segmentation of 4D dynamic upper airway MR images via fuzzy connectedness

“…The disadvantage of this approach is that much labor is required for building the 4D atlas. The second category is to adopt registration related techniques [11][12][13][14][16][17][18][19] which are the common approaches for 4D image segmentation by performing segmentation individually in each 3D volume. A 4D image is segmented by first manually carrying out segmentation of a 3D spatial volume corresponding to one time point (TP) and then propagating the manual segmentation to other time points to achieve segmentations at those time points.…”

Identification of Structures in Medical Images

“…As an example, the papillary muscles, the trabeculae, or the liver have pixel intensity values which are virtually indistinguishable from those of the myocardium (Cordero-Grande et al, 2011). In the literature there is a huge number of recent works on heart identification in MR images (Alattar et al, 2010;Cordero-Grande et al, 2011;Lekadir et al, 2011;O´Brien et al, 2011;O'Donnell et al, 2006;Rouchdy et al, 2007;Schaerer et al, 2010;Zhao et al, 2009). A www.intechopen.com comprehensive survey focusing on extracting the heart cavities in short axis view from cardiac MR images has been published recently by Petitjean & Dacher (2011).…”

“…Qian et al (2009) presented a method in which they try to avoid this common strong prior knowledge. The analysis of temporal change in vessel images was addressed by Zhao et al (2009) who described a method to identify subjects with connective tissue disorder in 4D cardiovascular MRI.…”

Automatic Abdominal Aortic Aneurysm segmentation in MR images

“…Other methods perform a full 3D segmentation in one single step, profiting from the usage of more information simultaneously (Ayyalasomayajula et al, 2010;Lee et al, 2010). It should also be noted that 4D methods (3D throughout the cardiac cycle) have been also proposed (Hameeteman et al, 2013;Zhao et al, 2009). …”