Aortic valve landmarks detection in Computed Tomography (CT) images is essential for planning Transcatheter Aortic Valve Implantation (TAVI) and establishing predictive factors of cardiac conduction disturbance. The fully automatic detection facilitates the measurement and planning while reducing interobserver variability. Although the emerged Convolutional Neural Networks (CNNs) have been applied to fully automatic landmark detection, it is challenging for CNNs to directly process large-scale CT volumes due to the limited computational resources. Some common preprocessing to deal with the limitation of resources, such as down-sampling or center cropping, can cause the volume to lose detailed features or global information. To address these issues, we propose a two-stage detection method based on CNN. The proposed method initially detects the approximate positions of the landmarks in the global view and then obtains refined results in local regions, without overdependence on prior knowledge or labor-intensive preprocessing. Eight important aortic valve landmarks, including three hinge points, three commissure points, the middle point of the cusps, and the point of the lower part of the membranous septum are automatically detected from our network. An overall result of Mean Radial Error (MRE) of 2.23 mm is yielded from our data set containing 150 individual cardiac CT volumes. The method takes 0.15 seconds per stage to process one volume, showing high efficiency.

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