  It has been widely recognized that the success of deep learning in image
segmentation relies overwhelmingly on a myriad amount of densely annotated
training data, which, however, are difficult to obtain due to the tremendous
labor and expertise required, particularly for annotating 3D medical images.
Although self-supervised learning (SSL) has shown great potential to address
this issue, most SSL approaches focus only on image-level global consistency,
but ignore the local consistency which plays a pivotal role in capturing
structural information for dense prediction tasks such as segmentation. In this
paper, we propose a PriorGuided Local (PGL) self-supervised model that learns
the region-wise local consistency in the latent feature space. Specifically, we
use the spatial transformations, which produce different augmented views of the
same image, as a prior to deduce the location relation between two views, which
is then used to align the feature maps of the same local region but being
extracted on two views. Next, we construct a local consistency loss to minimize
the voxel-wise discrepancy between the aligned feature maps. Thus, our PGL
model learns the distinctive representations of local regions, and hence is
able to retain structural information. This ability is conducive to downstream
segmentation tasks. We conducted an extensive evaluation on four public
computerized tomography (CT) datasets that cover 11 kinds of major human organs
and two tumors. The results indicate that using pre-trained PGL model to
initialize a downstream network leads to a substantial performance improvement
over both random initialization and the initialization with global
consistency-based models. Code and pre-trained weights will be made available
at: https://git.io/PGL.
