RFMedSAM 2：基于SAM 2的自动提示优化以增强体积医学图像分割
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
AI总结：
本文提出RFMedSAM 2，通过微调适配器探索SAM 2性能上限，并引入UNet自动生成提示以消除提示依赖，在BTCV和AMOS2022数据集上显著超越nnUNet。
摘要：
Segment Anything Model 2（SAM 2）是一种提示驱动的基座模型，将SAM扩展到图像和视频领域，与前一版本相比展现出更优越的零样本性能。基于SAM在医学图像分割中的成功，SAM 2显示出进一步发展的巨大潜力。然而，与SAM类似，SAM 2受限于其仅输出二值掩码、无法推断语义标签以及依赖目标对象区域的精确提示。此外，将SAM和SAM 2直接应用于医学图像分割任务会产生次优结果。本文通过定制微调适配器探索SAM 2的性能上限，在BTCV数据集上实现了92.30%的Dice相似系数（DSC），比最先进的nnUNet高出12%。随后，我们通过研究各种提示生成器来解决提示依赖问题。我们引入一个UNet来自主生成预测掩码和边界框，作为SAM 2的输入。随后SAM 2进行的双阶段优化进一步提升了性能。大量实验表明，我们的方法在AMOS2022数据集上取得了最先进的结果，与nnUNet相比Dice提高了2.9%，在BTCV数据集上比nnUNet高出6.4%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
