用先验影像增强SAM2，提升放疗肿瘤自动分割精度
SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2
- 引入先验MRI和标注作为上下文输入，改进提示鲁棒性
- 在肝、腹腔、脑部肿瘤上达到0.86~0.90的Dice分数
- 适合需要快速高精度分割的放疗临床场景
精准肿瘤分割对自适应放疗至关重要，但耗时且依赖人工。虽然基于提示的Segment Anything Model 2（SAM2）展现出潜力，但在肿瘤分割上仍存在精度不足问题。本文提出基于先验知识的数据增强策略：(1) 利用先前的MR图像及标注作为上下文输入；(2) 通过随机边界框扩展与掩码侵蚀/膨胀提升提示鲁棒性。所提出的SAM2-Aug模型在One-Seq-Liver数据集（31例肝癌患者，115张MRI）上微调，并在Mix-Seq-Abdomen（28例，88张MRI）和Mix-Seq-Brain（37例，86张MRI）上无重训练评估。结果表明，SAM2-Aug在所有数据集上均优于卷积、Transformer及提示驱动模型，实现0.86（肝）、0.89（腹腔）、0.90（脑）的Dice分数，展现出跨肿瘤类型与成像序列的强泛化能力，边界敏感指标亦有提升。结论：融入先验影像并增强提示多样性可显著提升分割准确率与泛化性。SAM2-Aug为放疗中的肿瘤分割提供了一种高效可靠的新方案。代码与模型将开源于https://github.com/apple1986/SAM2-Aug。
原文摘要 · Abstract (English)
Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART. Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random bounding box expansion and mask erosion/dilation. The resulting model, SAM2-Aug, was fine-tuned and tested on the One-Seq-Liver dataset (115 MRIs from 31 liver cancer patients), and evaluated without retraining on Mix-Seq-Abdomen (88 MRIs, 28 patients) and Mix-Seq-Brain (86 MRIs, 37 patients). Results: SAM2-Aug outperformed convolutional, transformer-based, and prompt-driven models across all datasets, achieving Dice scores of 0.86(liver), 0.89(abdomen), and 0.90(brain). It demonstrated strong generalization across tumor types and imaging sequences, with improved performance in boundary-sensitive metrics. Conclusions: Incorporating prior images and enhancing prompt diversity significantly boosts segmentation accuracy and generalizability. SAM2-Aug offers a robust, efficient solution for tumor segmentation in ART. Code and models will be released at https://github.com/apple1986/SAM2-Aug.
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