  Multi-organ segmentation, which identifies and separates different organs in
medical images, is a fundamental task in medical image analysis. Recently, the
immense success of deep learning motivated its wide adoption in multi-organ
segmentation tasks. However, due to expensive labor costs and expertise, the
availability of multi-organ annotations is usually limited and hence poses a
challenge in obtaining sufficient training data for deep learning-based
methods. In this paper, we aim to address this issue by combining off-the-shelf
single-organ segmentation models to develop a multi-organ segmentation model on
the target dataset, which helps get rid of the dependence on annotated data for
multi-organ segmentation. To this end, we propose a novel dual-stage method
that consists of a Model Adaptation stage and a Model Ensemble stage. The first
stage enhances the generalization of each off-the-shelf segmentation model on
the target domain, while the second stage distills and integrates knowledge
from multiple adapted single-organ segmentation models. Extensive experiments
on four abdomen datasets demonstrate that our proposed method can effectively
leverage off-the-shelf single-organ segmentation models to obtain a tailored
model for multi-organ segmentation with high accuracy.
