  Deep neural networks yield promising results in a wide range of computer
vision applications, including landmark detection. A major challenge for
accurate anatomical landmark detection in volumetric images such as clinical CT
scans is that large-scale data often constrain the capacity of the employed
neural network architecture due to GPU memory limitations, which in turn can
limit the precision of the output. We propose a multi-scale, end-to-end deep
learning method that achieves fast and memory-efficient landmark detection in
3D images. Our architecture consists of blocks of shift-equivariant networks,
each of which performs landmark detection at a different spatial scale. These
blocks are connected from coarse to fine-scale, with differentiable resampling
layers, so that all levels can be trained together. We also present a noise
injection strategy that increases the robustness of the model and allows us to
quantify uncertainty at test time. We evaluate our method for carotid artery
bifurcations detection on 263 CT volumes and achieve a better than
state-of-the-art accuracy with mean Euclidean distance error of 2.81mm.
