  The task of labeling multiple organs for segmentation is a complex and
time-consuming process, resulting in a scarcity of comprehensively labeled
multi-organ datasets while the emergence of numerous partially labeled
datasets. Current methods are inadequate in effectively utilizing the
supervised information available from these datasets, thereby impeding the
progress in improving the segmentation accuracy. This paper proposes a
two-stage multi-organ segmentation method based on mutual learning, aiming to
improve multi-organ segmentation performance by complementing information among
partially labeled datasets. In the first stage, each partial-organ segmentation
model utilizes the non-overlapping organ labels from different datasets and the
distinct organ features extracted by different models, introducing additional
mutual difference learning to generate higher quality pseudo labels for
unlabeled organs. In the second stage, each full-organ segmentation model is
supervised by fully labeled datasets with pseudo labels and leverages true
labels from other datasets, while dynamically sharing accurate features across
different models, introducing additional mutual similarity learning to enhance
multi-organ segmentation performance. Extensive experiments were conducted on
nine datasets that included the head and neck, chest, abdomen, and pelvis. The
results indicate that our method has achieved SOTA performance in segmentation
tasks that rely on partial labels, and the ablation studies have thoroughly
confirmed the efficacy of the mutual learning mechanism.
