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，然而获取并维护带有密集标注的大规模数据集具有挑战性。半监督学习（SSL）是应对这些挑战的常用技术。近期，分割一切模型（SAM）在某些医学图像分割任务中展现出良好性能，但在肝脏肿瘤分割中表现欠佳。本文提出一种名为ASLseg的新型半监督框架，该框架可有效将SAM适配至SSL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SSL范式训练的分割模型提供生成的伪标签作为微调SAM的提示，再利用适配网络优化SAM预测结果并生成更高质量的伪标签。最终，选取可靠的伪标签扩展标注集以实现迭代训练。在LiTS数据集上的大量实验表明，我们的ASLseg方法展现了卓越性能。