RT Journal Article
T1 Deep learning method for aortic root detection
A1 García Tahoces, Pablo
A1 Varela, Rafael
A1 Carreira Villamor, José Martín
K1 Computed tomography angiography (CTA)
K1 Aortic root
K1 Vascular imaging
K1 Detection
K1 Landmarks
AB Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior.Methods: A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance.Results: The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. Conclusions: From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentation
PB Elsevier
SN 0010-4825
YR 2021
FD 2021
LK http://hdl.handle.net/10347/26513
UL http://hdl.handle.net/10347/26513
LA eng
NO Computers in Biology and Medicine, 135 (2021), 104533. https://doi.org/10.1016/j.compbiomed.2021.104533
NO This work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19)
DS Minerva
RD 28 abr 2026