Abdominal Multi-organ Segmentation Using CNN and Transformer

In this paper, we combine the advantages of convolution local correlation and translation invariance in CNN with Transformer's ability to effectively capture long-term dependencies between pixels to produce high-quality pseudo labels. In order to segment images efficiently and quickly, we select nnU-Net [2] as the final segmentation network and use pseudo labels, unlabeled data and labeled data together to train the network, and then we use Generic U-Net [2], the backbone network of nnU-Net, as final prediction network. The mean DSC of the prediction results of our method on validation set of FLARE2022 Challenge [3] is 0.7580.