Semi-supervised Multi-organ Segmentation with Cross Supervision Using Siamese Network

Numerous unlabeled data is useful for supervised medical image segmentation, if the labeled data is limited. To leverage all the unlabeled images for efficient abdominal organ segmentation, we developed semi-supervised framework with cross supervision using siamese network, i.e., SemiSeg-CSSN. Cross supervision enables the two networks to optimize the network using pseudo-labels generated by the other. Moreover, we applied the cascade strategy for the task because of the large and uncertain locations of the abdomen regions. To validate the effects of unlabeled data, we employed an unlabeled image filtering strategy to select the unlabeled image and their pseudo label images with low uncertainty. On the FLARE2022 validation cases, with the help of unlabeled data, our method obtained the average dice similarity coefficient (DSC) of 77.7% and average normalized surface distance (NSD) of 82.0%, which is better than the supervised method. The average running time is 12.9 s per case in inference phase and maximum used GPU memory is 2052 MB.