Computed tomography angiography (CTA) is one of the most common vascular imaging modalities. However, for clinical use, it still requires laborious manual analysis. This study demonstrates the feasibility of a fully automated technology for the accurate detection and identification of several anatomical reference points (landmarks), commonly used in intravascular imaging. This technology uses two different approaches, specially designed for the detection of aortic root and supra-aortic and visceral branches. In order to adjust the parameters of the developed algorithms, a total of 33 computed tomography scans with different types of pathologies were selected. Furthermore, a total of 30 independently selected computed tomography scans were used to assess their performance. Accuracy was evaluated by comparing the locations of reference points manually marked by human experts with those that were automatically detected. For supra-aortic and visceral branches detection, average values of 91.8 % for recall and 98.8 % for precision were obtained. For aortic root detection, the average difference between the positions marked by the experts and those detected by the computer was 5.7 ± 7.3 mm. Finally, diameters and lengths of the aorta were measured at different locations related to the extracted landmarks. Those measurements agreed with the values reported by the literature. Graphical abstract Schematic description of the proposed algorithm. The input includes an already segmented aorta (left), there are two main sub-processes related to the detection of branches and roots (center), and the output includes the segmented original aorta with the branches and the detected landmarks superimposed (right).
Keywords: Aortic branches; Aortic root; Computed tomography (CT); Detection; Vessel morphology.