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.

翻译：肝脏肿瘤分割对于计算机辅助诊断、手术规划及预后评估至关重要。然而，获取并维护大规模密集标注数据集极具挑战性。半监督学习（SSL）是应对这些难题的常用技术。近期，分割一切模型（SAM）在部分医学图像分割任务中展现出令人瞩目的性能，但在肝脏肿瘤分割中表现欠佳。本文提出一种新型半监督框架ASLseg，该框架能有效将SAM适配至半监督学习场景，并融合肝脏肿瘤的领域特定知识与通用知识。具体而言，通过特定半监督学习范式训练的分割模型将生成的伪标签作为提示输入至微调后的SAM，随后利用自适应网络优化SAM预测结果以生成更高质量的伪标签。最终选取可靠伪标签扩充标注集进行迭代训练。在LiTS数据集上的大量实验表明，本文提出的ASLseg框架具有显著优越性能。