  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 NSD 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. The
code is available at https://github.com/lseventeen/FLARE22-TwoStagePHTrans.
