  Multi-organ segmentation has extensive applications in many clinical
applications. To segment multiple organs of interest, it is generally quite
difficult to collect full annotations of all the organs on the same images, as
some medical centers might only annotate a portion of the organs due to their
own clinical practice. In most scenarios, one might obtain annotations of a
single or a few organs from one training set, and obtain annotations of the the
other organs from another set of training images. Existing approaches mostly
train and deploy a single model for each subset of organs, which are memory
intensive and also time inefficient. In this paper, we propose to co-train
weight-averaged models for learning a unified multi-organ segmentation network
from few-organ datasets. We collaboratively train two networks and let the
coupled networks teach each other on un-annotated organs. To alleviate the
noisy teaching supervisions between the networks, the weighted-averaged models
are adopted to produce more reliable soft labels. In addition, a novel region
mask is utilized to selectively apply the consistent constraint on the
un-annotated organ regions that require collaborative teaching, which further
boosts the performance. Extensive experiments on three public available
single-organ datasets LiTS, KiTS, Pancreas and manually-constructed
single-organ datasets from MOBA show that our method can better utilize the
few-organ datasets and achieves superior performance with less inference
computational cost.
