  We present an automated method for localizing an anatomical landmark in
three-dimensional medical images. The method combines two recurrent neural
networks in a coarse-to-fine approach: The first network determines a candidate
neighborhood by analyzing the complete given image volume. The second network
localizes the actual landmark precisely and accurately in the candidate
neighborhood. Both networks take advantage of multi-dimensional gated recurrent
units in their main layers, which allow for high model complexity with a
comparatively small set of parameters. We localize the medullopontine sulcus in
3D magnetic resonance images of the head and neck. We show that the proposed
approach outperforms similar localization techniques both in terms of mean
distance in millimeters and voxels w.r.t. manual labelings of the data. With a
mean localization error of 1.7 mm, the proposed approach performs on par with
neurological experts, as we demonstrate in an interrater comparison.
