Abstract
To perform aortic valve morphology for the assessment of the valvular heart disease in cardiovascular medicine, an accurate identification of specific anatomical points, i.e. landmarks, which define the aortic cusps, is required. In this study, we investigate the application of a deep learning framework, namely the spatial configuration network, for aortic cusp landmark detection in 120 contrast-enhanced end-diastolic coronary computed tomography images of normal patients. By performing three-fold cross-validation experiments, we obtained a mean detection error of \(1.45\,{\pm }\,0.82\) mm for six landmarks located at the nadirs and commissures of the aortic valve sinuses, which dropped to \(1.15\,{\pm }\,0.62\) mm when landmarks were detected in images that were cropped around the aortic valve by applying atlas-based segmentation. The obtained accuracy is comparable to existing methods, however, additional improvements in the form of image pre- or post-processing, or by applying advanced methodological concepts, may improve the landmark detection performance.
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Acknowledgements
The study was approved by the Ethics Committee of the University Medical Center Ljubljana, Slovenia, under 0120-133/2021/3 and 0120-312/2022/3, and supported by the Slovenian Research Agency (ARRS) under grants J2-4453 and P2-0232, and by the University Medical Center Ljubljana, Slovenia, under grant 20190174.
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Škrlj, L., Jelenc, M., Vrtovec, T. (2023). Detection of Aortic Cusp Landmarks in Computed Tomography Images with Deep Learning. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_31
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