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Automatic detection of anatomical landmarks of the aorta in CTA images

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Abstract

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.

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).

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Acknowledgments

This research has partially been supported by the MINECO projects references TIN2016-76373-P (AEI/FEDER, UE) and MTM2016-75339-P (AEI/FEDER, UE) (Ministerio de Economía y Competitividad, Spain).

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Authors and Affiliations

  1. Department Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain

    Pablo G. Tahoces

  2. CTIM, DIS, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Daniel Santana-Cedrés, Luis Alvarez, Miguel Alemán-Flores, Agustín Trujillo & Carmelo Cuenca

  3. Complejo Hospitalario Universitario de Santiago (CHUS), Santiago de Compostela, Spain

    Jose M. Carreira

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  1. Pablo G. Tahoces
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  2. Daniel Santana-Cedrés
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  5. Agustín Trujillo
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Correspondence to Pablo G. Tahoces.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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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

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  • DOI: https://doi.org/10.1007/s11517-019-02110-x

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