用自动生成提示增强SAM2，让医学影像分割更准更快
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
- 用UNet自动生成提示框和掩码，减少人工干预
- 在BTCV数据集上达92.3%的分割精度，比nnUNet高12%
- 适合需要高精度自动分割的医学影像研究者
Segment Anything Model 2（SAM 2）作为扩展至图像与视频领域的提示驱动基础模型，在零样本性能上优于前代。尽管其在医学图像分割中展现潜力，但依然存在输出二值掩码、无法推断语义标签以及依赖精确提示等局限。直接应用于医学分割任务时效果不佳。本文通过定制微调适配器，将SAM 2在BTCV数据集上的Dice相似系数（DSC）提升至92.30%，超过state-of-the-art的nnUNet 12%。进一步提出使用UNet自动生成预测掩码与边界框作为输入，经SAM 2双阶段精炼后，实现更优分割效果。在AMOS2022数据集上相比nnUNet提升2.9% Dice，在BTCV上领先6.4%。
原文摘要 · Abstract (English)
Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor. Building on SAM's success in medical image segmentation, SAM 2 presents significant potential for further advancement. However, similar to SAM, SAM 2 is limited by its output of binary masks, inability to infer semantic labels, and dependence on precise prompts for the target object area. Additionally, direct application of SAM and SAM 2 to medical image segmentation tasks yields suboptimal results. In this paper, we explore the upper performance limit of SAM 2 using custom fine-tuning adapters, achieving a Dice Similarity Coefficient (DSC) of 92.30% on the BTCV dataset, surpassing the state-of-the-art nnUNet by 12%. Following this, we address the prompt dependency by investigating various prompt generators. We introduce a UNet to autonomously generate predicted masks and bounding boxes, which serve as input to SAM 2. Subsequent dual-stage refinements by SAM 2 further enhance performance. Extensive experiments show that our method achieves state-of-the-art results on the AMOS2022 dataset, with a Dice improvement of 2.9% compared to nnUNet, and outperforms nnUNet by 6.4% on the BTCV dataset.
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