ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor
  Segmentation
Shiyun Chen Li Lin Pujin Cheng Xiaoying Tang
Liver tumor segmentation is essential for computer-aided diagnosis, surgical
planning, and prognosis evaluation. However, obtaining and maintaining a
large-scale dataset with dense annotations is challenging. Semi-Supervised
Learning (SSL) is a common technique to address these challenges. Recently,
Segment Anything Model (SAM) has shown promising performance in some medical
image segmentation tasks, but it performs poorly for liver tumor segmentation.
In this paper, we propose a novel semi-supervised framework, named ASLseg,
which can effectively adapt the SAM to the SSL setting and combine both
domain-specific and general knowledge of liver tumors. Specifically, the
segmentation model trained with a specific SSL paradigm provides the generated
pseudo-labels as prompts to the fine-tuned SAM. An adaptation network is then
used to refine the SAM-predictions and generate higher-quality pseudo-labels.
Finally, the reliable pseudo-labels are selected to expand the labeled set for
iterative training. Extensive experiments on the LiTS dataset demonstrate
overwhelming performance of our ASLseg.