  Deep learning networks have shown promising performance for accurate object
localization in medial images, but require large amount of annotated data for
supervised training, which is expensive and expertise burdensome. To address
this problem, we present a one-shot framework for organ and landmark
localization in volumetric medical images, which does not need any annotation
during the training stage and could be employed to locate any landmarks or
organs in test images given a support (reference) image during the inference
stage. Our main idea comes from that tissues and organs from different human
bodies have a similar relative position and context. Therefore, we could
predict the relative positions of their non-local patches, thus locate the
target organ. Our framework is composed of three parts: (1) A projection
network trained to predict the 3D offset between any two patches from the same
volume, where human annotations are not required. In the inference stage, it
takes one given landmark in a reference image as a support patch and predicts
the offset from a random patch to the corresponding landmark in the test
(query) volume. (2) A coarse-to-fine framework contains two projection
networks, providing more accurate localization of the target. (3) Based on the
coarse-to-fine model, we transfer the organ boundingbox (B-box) detection to
locating six extreme points along x, y and z directions in the query volume.
Experiments on multi-organ localization from head-and-neck (HaN) CT volumes
showed that our method acquired competitive performance in real time, which is
more accurate and 10^5 times faster than template matching methods with the
same setting. Code is available: https://github.com/LWHYC/RPR-Loc.
