  High annotation costs and limited labels for dense 3D medical imaging tasks
have recently motivated an assortment of 3D self-supervised pretraining methods
that improve transfer learning performance. However, these methods commonly
lack spatial awareness despite its centrality in enabling effective 3D image
analysis. More specifically, position, scale, and orientation are not only
informative but also automatically available when generating image crops for
training. Yet, to date, no work has proposed a pretext task that distills all
key spatial features. To fulfill this need, we develop a new self-supervised
method, VectorPOSE, which promotes better spatial understanding with two novel
pretext tasks: Vector Prediction (VP) and Boundary-Focused Reconstruction
(BFR). VP focuses on global spatial concepts (i.e., properties of 3D patches)
while BFR addresses weaknesses of recent reconstruction methods to learn more
effective local representations. We evaluate VectorPOSE on three 3D medical
image segmentation tasks, showing that it often outperforms state-of-the-art
methods, especially in limited annotation settings.
