Automatic characterization of thoracic aortic aneurysms from CT images.

Accurate determination of the diameter is an important step for diagnosis and follow-up of aortic abnormalities such as aneurysms, caused by dilation of the vessel lumen. In this work we focus on the development of an automatic method for measuring the calibre of the thoracic aorta. The method is based on the application of principal component analysis on normal planes extracted from the aorta to establish the main axis of each section of the vessel. Two experiments were performed in order to test the accuracy and the rotational invariance of the developed method. Accuracy was determined by using a database of 15 clinical cases, where our method and a commercial software, which was considered as the gold standard, were compared. For the rotational invariance check, phantom images in different orientations were obtained and the diameter was measured with the proposed method. For clinical cases, a good agreement was observed between our method and the gold standard. The Bland Altman plots indicated that all of the values were within the acceptable limits of agreement with a bias of 0.2mm between both methods. For phantom cases, an ANOVA test revealed that the results achieved for the data sets acquired for the different orientations were not statistically different (F=1.88, p=0.153), which demonstrates the robustness of the method for rotations. The proposed method is applicable for measuring the diameter in all tested cases, and the results achieved underscored the capability of our approach for automatic characterization of thoracic aortic aneurysms.

A hybrid method based on level set and 3D region growing for segmentation of the thoracic aorta.

This study sought to develop a completely automatic method for image segmentation of the thoracic aorta. We used a total of 4682 images from 10 consecutive patients. The proposed method is based on the use of level set and region growing, automatically initialized using the Hough transform. The results obtained were compared to those of manual segmentation as performed by an external expert radiologist. Concordance between the developed method and manual segmentation ranged from 92.79 to 95.77% in the descending regions of the aorta and from 90.68 to 96.54% in the ascending regions, with a mean value of 93.83% being obtained for total segmentation.