  Anatomical landmarks are a crucial prerequisite for many medical imaging
tasks. Usually, the set of landmarks for a given task is predefined by experts.
The landmark locations for a given image are then annotated manually or via
machine learning methods trained on manual annotations. In this paper, in
contrast, we present a method to automatically discover and localize anatomical
landmarks in medical images. Specifically, we consider landmarks that attract
the visual attention of humans, which we term visually salient landmarks. We
illustrate the method for fetal neurosonographic images. First, full-length
clinical fetal ultrasound scans are recorded with live sonographer
gaze-tracking. Next, a convolutional neural network (CNN) is trained to predict
the gaze point distribution (saliency map) of the sonographers on scan video
frames. The CNN is then used to predict saliency maps of unseen fetal
neurosonographic images, and the landmarks are extracted as the local maxima of
these saliency maps. Finally, the landmarks are matched across images by
clustering the landmark CNN features. We show that the discovered landmarks can
be used within affine image registration, with average landmark alignment
errors between 4.1% and 10.9% of the fetal head long axis length.
