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Large curated datasets are necessary, but annotating medical images is atime-consuming, laborious, and expensive process. Therefore, recent supervisedmethods are focusing on utilizing a large amount of unlabeled data. However, todo so, is a challenging task. To address this problem, we propose a new 3DCross-Pseudo Supervision (3D-CPS) method, a semi-supervised networkarchitecture based on nnU-Net with the Cross-Pseudo Supervision method. Wedesign a new nnU-Net based preprocessing. In addition, we set thesemi-supervised loss weights to expand linearity with each epoch to prevent themodel from low-quality pseudo-labels in the early training process. Ourproposed method achieves an average dice similarity coefficient (DSC) of 0.881and an average normalized surface distance (NSD) of 0.913 on the MICCAIFLARE2022 validation set (20 cases).

3D Cross-Pseudo Supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation