  Large curated datasets are necessary, but annotating medical images is a
time-consuming, laborious, and expensive process. Therefore, recent supervised
methods are focusing on utilizing a large amount of unlabeled data. However, to
do so, is a challenging task. To address this problem, we propose a new 3D
Cross-Pseudo Supervision (3D-CPS) method, a semi-supervised network
architecture based on nnU-Net with the Cross-Pseudo Supervision method. We
design a new nnU-Net based preprocessing. In addition, we set the
semi-supervised loss weights to expand linearity with each epoch to prevent the
model from low-quality pseudo-labels in the early training process. Our
proposed method achieves an average dice similarity coefficient (DSC) of 0.881
and an average normalized surface distance (NSD) of 0.913 on the MICCAI
FLARE2022 validation set (20 cases).
