Deep learning method for aortic root detection

Autor: José M. Carreira, Rafael Varela, Pablo G. Tahoces
Přispěvatelé: Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Rok vydání: 2021
Předmět:
Zdroj: Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname
Minerva: Repositorio Institucional de la Universidad de Santiago de Compostela
Universidad de Santiago de Compostela (USC)
ISSN: 0010-4825
Popis: 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 This work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19) SI
Databáze: OpenAIRE