  We focus on an important yet challenging problem: using a 2D deep network to
deal with 3D segmentation for medical image analysis. Existing approaches
either applied multi-view planar (2D) networks or directly used volumetric (3D)
networks for this purpose, but both of them are not ideal: 2D networks cannot
capture 3D contexts effectively, and 3D networks are both memory-consuming and
less stable arguably due to the lack of pre-trained models.
  In this paper, we bridge the gap between 2D and 3D using a novel approach
named Elastic Boundary Projection (EBP). The key observation is that, although
the object is a 3D volume, what we really need in segmentation is to find its
boundary which is a 2D surface. Therefore, we place a number of pivot points in
the 3D space, and for each pivot, we determine its distance to the object
boundary along a dense set of directions. This creates an elastic shell around
each pivot which is initialized as a perfect sphere. We train a 2D deep network
to determine whether each ending point falls within the object, and gradually
adjust the shell so that it gradually converges to the actual shape of the
boundary and thus achieves the goal of segmentation. EBP allows boundary-based
segmentation without cutting a 3D volume into slices or patches, which stands
out from conventional 2D and 3D approaches. EBP achieves promising accuracy in
abdominal organ segmentation. Our code has been open-sourced
https://github.com/twni2016/Elastic-Boundary-Projection.
