Deep-learning (DL) based methods are playing an important role in the task of
abdominal organs and tumors segmentation in CT scans. However, the large
requirements of annotated datasets heavily limit its development. The FLARE23
challenge provides a large-scale dataset with both partially and fully
annotated data, which also focuses on both segmentation accuracy and
computational efficiency. In this study, we propose to use the strategy of
Semi-Supervised Learning (SSL) and iterative pseudo labeling to address
FLARE23. Initially, a deep model (nn-UNet) trained on datasets with complete
organ annotations (about 220 scans) generates pseudo labels for the whole
dataset. These pseudo labels are then employed to train a more powerful
segmentation model. Employing the FLARE23 dataset, our approach achieves an
average DSC score of 89.63% for organs and 46.07% for tumors on online
validation leaderboard. For organ segmentation, We obtain 0.9007\% DSC and
0.9493\% NSD. For tumor segmentation, we obtain 0.3785% DSC and 0.2842% NSD.
Our code is available at this https URL

在这项研究中，我们提出了使用半监督学习和迭代伪标签策略来解决 FLARE23 挑战，在通过完整器官注释数据集训练的深度模型 (nn-UNet) 生成伪标签后，在 FLARE23 数据集上，我们的方法在器官分割和肿瘤分割上取得了优秀的结果。