  Landmark detection is a critical component of the image processing pipeline
for automated aortic size measurements. Given that the thoracic aorta has a
relatively conserved topology across the population and that a human annotator
with minimal training can estimate the location of unseen landmarks from
limited examples, we proposed an auxiliary learning task to learn the implicit
topology of aortic landmarks through a CNN-based network. Specifically, we
created a network to predict the location of missing landmarks from the visible
ones by minimizing the Implicit Topology loss in an end-to-end manner. The
proposed learning task can be easily adapted and combined with Unet-style
backbones. To validate our method, we utilized a dataset consisting of 207
CTAs, labeling four landmarks on each aorta. Our method outperforms the
state-of-the-art Unet-style architectures (ResUnet, UnetR) in terms of
localization accuracy, with only a light (#params=0.4M) overhead. We also
demonstrate our approach in two clinically meaningful applications: aortic
sub-region division and automatic centerline generation.
