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Updated: Nov 1, 2025
Full-root Aortic Valve Replacement by Stentless Aortic Xenografts in Patients with Small Aortic Roots
Published on: May 21, 2017
Deep learning method for aortic root detection
Pablo G Tahoces1, Rafael Varela2, Jose M Carreira2
1Department of Electronics and Computer Science, Universidad de Santiago de Compostela, Santiago de Compostela, Spain.
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.
