用SAM生成伪标签，少标注数据也能精准分割医学影像
SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation
- 用少量标注数据生成框和点，驱动SAM产生伪标签
- 骨与牙分割的Dice分数分别提升至84.17%和74.87%
- 适合标注稀缺的医疗图像分割场景
语义分割在医学影像中至关重要。尽管监督学习表现良好，但依赖大量标注数据。近期提出的Segment Anything Model（SAM）支持提示式分割，具备对未知对象的零样本泛化能力。本文利用SAM的抽象对象理解能力，为医学图像分割提供伪标签，缓解标注数据不足问题。方法基于少量标注数据（最多43例）提取边界框和种子点作为提示输入SAM，生成密集分割掩码作为未标注数据的伪标签。实验表明，使用该伪标签训练后，儿童手腕骨骼和牙科X光片中牙齿的分割Dice分数分别从74.29%提升至84.17%，从66.63%提升至74.87%。结果优于基于强度的后处理、先进的监督模型nnU-Net以及半监督均值教师方法。代码已开源。
原文摘要 · Abstract (English)
Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment Anything Model (SAM) enables prompt-based segmentation and offers zero-shot generalization to unfamiliar objects. In our work, we leverage SAM's abstract object understanding for medical image segmentation to provide pseudo labels for semi-supervised learning, thereby mitigating the need for extensive annotated training data. Our approach refines initial segmentations that are derived from a limited amount of annotated data (comprising up to 43 cases) by extracting bounding boxes and seed points as prompts forwarded to SAM. Thus, it enables the generation of dense segmentation masks as pseudo labels for unlabelled data. The results show that training with our pseudo labels yields an improvement in Dice score from $74.29\,\%$ to $84.17\,\%$ and from $66.63\,\%$ to $74.87\,\%$ for the segmentation of bones of the paediatric wrist and teeth in dental radiographs, respectively. As a result, our method outperforms intensity-based post-processing methods, state-of-the-art supervised learning for segmentation (nnU-Net), and the semi-supervised mean teacher approach. Our Code is available on GitHub.
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