  Purpose: Automated distinct bone segmentation from CT scans is widely used in
planning and navigation workflows. U-Net variants are known to provide
excellent results in supervised semantic segmentation. However, in distinct
bone segmentation from upper body CTs a large field of view and a
computationally taxing 3D architecture are required. This leads to
low-resolution results lacking detail or localisation errors due to missing
spatial context when using high-resolution inputs.
  Methods: We propose to solve this problem by using end-to-end trainable
segmentation networks that combine several 3D U-Nets working at different
resolutions. Our approach, which extends and generalizes HookNet and MRN,
captures spatial information at a lower resolution and skips the encoded
information to the target network, which operates on smaller high-resolution
inputs. We evaluated our proposed architecture against single resolution
networks and performed an ablation study on information concatenation and the
number of context networks.
  Results: Our proposed best network achieves a median DSC of 0.86 taken over
all 125 segmented bone classes and reduces the confusion among similar-looking
bones in different locations. These results outperform our previously published
3D U-Net baseline results on the task and distinct-bone segmentation results
reported by other groups.
  Conclusion: The presented multi-resolution 3D U-Nets address current
shortcomings in bone segmentation from upper-body CT scans by allowing for
capturing a larger field of view while avoiding the cubic growth of the input
pixels and intermediate computations that quickly outgrow the computational
capacities in 3D. The approach thus improves the accuracy and efficiency of
distinct bone segmentation from upper-body CT.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning