Abdominal multi-organ segmentation in computed tomography (CT) scans has exhibited successful applications in numerous real clinical scenarios. Nevertheless, prevailing methods for multi-organ segmentation often necessitate either a substantial volume of datasets derived from a single healthcare institution or the centralized storage of patient data obtained from diverse healthcare institutions. This prevailing approach significantly burdens data labeling and collection, thereby exacerbating the associated challenges. Compared to multi organ annotation labels, single organ annotation labels are extremely easy to obtain and have low costs. Therefor, this work establishes an effective collaborative mechanism between multi organ labels and single organ labels, and proposes an expert guided and partially-labeled data collaboration framework for multi organ segmentation, named EGPD-Seg. Firstly, a reward penalty loss function is proposed under the setting of partial labels to make the model more focused on the targets in single organ labels, while suppressing the influence of unlabeled organs on segmentation results. Then, an expert guided module is proposed to enable the model to learn prior knowledge, thereby enabling the model to obtain the ability to segment unlabeled organs on a single organ labeled dataset. The two modules interact with each other and jointly promote the multi organ segmentation performance of the model under label partial settings. This work has been effectively validated on five publicly available abdominal multi organ segmentation datasets, including internal datasets and invisible external datasets. Code: https://github.com/LiLiXJTU/EGPDC-Seg.
