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We present an automated method for localizing an anatomical landmark inthree-dimensional medical images. The method combines two recurrent neuralnetworks in a coarse-to-fine approach: The first network determines a candidateneighborhood by analyzing the complete given image volume. The second networklocalizes the actual landmark precisely and accurately in the candidateneighborhood. Both networks take advantage of multi-dimensional gated recurrentunits in their main layers, which allow for high model complexity with acomparatively small set of parameters. We localize the medullopontine sulcus in3D magnetic resonance images of the head and neck. We show that the proposedapproach outperforms similar localization techniques both in terms of meandistance in millimeters and voxels w.r.t. manual labelings of the data. With amean localization error of 1.7 mm, the proposed approach performs on par withneurological experts, as we demonstrate in an interrater comparison.

Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization