RT Journal Article
T1 Automatic detection of anatomical landmarks of the aorta in CTA images
A1 García Tahoces, Pablo
A1 Santana Cedrés, Daniel
A1 Álvarez León, Luis Miguel
A1 Alemán Flores, Miguel
A1 Trujillo Pino, Agustín
A1 Cuenca Hernández, Carmelo
A1 Carreira Villamor, José Martin
K1 Computed tomography (CT)
K1 Aortic root
K1 Aortic branches
K1 Detection
K1 Vessel morphology
AB 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
PB Springer Nature
SN 0140-0118
YR 2020
FD 2020-02-19
LK https://hdl.handle.net/10347/40507
UL https://hdl.handle.net/10347/40507
LA eng
NO Tahoces, P.G., Santana-Cedrés, D., Alvarez, L. et al. Automatic detection of anatomical landmarks of the aorta in CTA images. Med Biol Eng Comput 58, 903–919 (2020). https://doi.org/10.1007/s11517-019-02110-x
NO This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11517-019-02110-x
DS Minerva
RD 24 abr 2026