  Learning by imitation is one of the most significant abilities of human
beings and plays a vital role in human's computational neural system. In
medical image analysis, given several exemplars (anchors), experienced
radiologist has the ability to delineate unfamiliar organs by imitating the
reasoning process learned from existing types of organs. Inspired by this
observation, we propose OrganNet which learns a generalized organ concept from
a set of annotated organ classes and then transfer this concept to unseen
classes. In this paper, we show that such process can be integrated into the
one-shot segmentation task which is a very challenging but meaningful topic. We
propose pyramid reasoning modules (PRMs) to model the anatomical correlation
between anchor and target volumes. In practice, the proposed module first
computes a correlation matrix between target and anchor computerized tomography
(CT) volumes. Then, this matrix is used to transform the feature
representations of both anchor volume and its segmentation mask. Finally,
OrganNet learns to fuse the representations from various inputs and predicts
segmentation results for target volume. Extensive experiments show that
OrganNet can effectively resist the wide variations in organ morphology and
produce state-of-the-art results in one-shot segmentation task. Moreover, even
when compared with fully-supervised segmentation models, OrganNet is still able
to produce satisfying segmentation results.
