  Most existing approaches to train a unified multi-organ segmentation model
from several single-organ datasets require simultaneously access multiple
datasets during training. In the real scenarios, due to privacy and ethics
concerns, the training data of the organs of interest may not be publicly
available. To this end, we investigate a data-free incremental organ
segmentation scenario and propose a novel incremental training framework to
solve it. We use the pretrained model instead of its own training data for
privacy protection. Specifically, given a pretrained $K$ organ segmentation
model and a new single-organ dataset, we train a unified $K+1$ organ
segmentation model without accessing any data belonging to the previous
training stages. Our approach consists of two parts: the background label
alignment strategy and the uncertainty-aware guidance strategy. The first part
is used for knowledge transfer from the pretained model to the training model.
The second part is used to extract the uncertainty information from the
pretrained model to guide the whole knowledge transfer process. By combing
these two strategies, more reliable information is extracted from the
pretrained model without original training data. Experiments on multiple
publicly available pretrained models and a multi-organ dataset MOBA have
demonstrated the effectiveness of our framework.
