  Annotating multiple organs in 3D medical images is time-consuming and costly.
Meanwhile, there exist many single-organ datasets with one specific organ
annotated. This paper investigates how to learn a multi-organ segmentation
model leveraging a set of binary-labeled datasets. A novel Multi-teacher
Single-student Knowledge Distillation (MS-KD) framework is proposed, where the
teacher models are pre-trained single-organ segmentation networks, and the
student model is a multi-organ segmentation network. Considering that each
teacher focuses on different organs, a region-based supervision method,
consisting of logits-wise supervision and feature-wise supervision, is
proposed. Each teacher supervises the student in two regions, the organ region
where the teacher is considered as an expert and the background region where
all teachers agree. Extensive experiments on three public single-organ datasets
and a multi-organ dataset have demonstrated the effectiveness of the proposed
MS-KD framework.
