Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manually annotating organs from CT scans is time-consuming and labor-intensive. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we follow the self-training strategy and employ a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unlabeled data. Afterward, we introduce them with labeled data together into a two-stage segmentation framework with lightweight PHTrans for training to improve the performance and generalization ability of the model while remaining efficient. Experiments on the validation set of FLARE2022 demonstrate that our method achieves excellent segmentation performance as well as fast and low-resource model inference. The average DSC and HSD are 0.8956 and 0.9316, respectively. Under our development environments, the average inference time is 18.62 s, the average maximum GPU memory is 1995.04 MB, and the area under the GPU memory-time curve and the average area under the CPU utilization-time curve are 23196.84 and 319.67.

翻译：在这项工作中,我们遵循自我培训战略,并采用高性能混合结构(PHTrans),由CNN和Swin变异器组成,教师模型为未贴标签的数据制作精确的假标签。之后,我们将其与标记的CT扫描器官一起引入两阶段分解框架,采用轻重量的PHTrans进行培训,以提高模型的性能和通用能力,同时保持效率。 FLARE2022校准集的实验显示,我们的方法取得了极好的分解性能,以及快速和低资源模型。平均DSC和HSDF分别为0.8956和0.9316。在我们的发展环境中,平均时间为18.62秒,平均GPU记忆量为1995年平均和1918.84年平均时间的CPU值,以及GPU下的平均纬度为23.84年的平均纬度。