提升3D医学图像分割效果，自动获取提示并增强空间特征。
DEAP-3DSAM: Decoder Enhanced and Auto Prompt SAM for 3D Medical Image Segmentation
- 用增强解码器融合原始特征与空间信息，强化三维感知。
- 设计双注意力提示器，实现无需人工干预的自动提示生成。
- 在四个腹部肿瘤数据集上达顶尖性能，适合临床实用场景。
分割一切模型（SAM）在医学图像分割中展现出巨大潜力。尽管SAM主要基于2D图像训练，已有研究尝试将其应用于3D医学图像分割，但采用伪3D处理方式导致空间特征丢失，限制了性能表现。此外，多数基于SAM的方法仍依赖人工提示，在真实场景中难以实施，且需大量专家知识。为此，我们提出解码器增强与自动提示的SAM（DEAP-3DSAM）以解决上述问题。具体而言，我们设计了特征增强解码器，融合原始图像特征与丰富详细的三维空间信息，以增强空间表达能力；同时提出双注意力提示器，通过空间注意力与通道注意力自动获取提示信息。我们在四个公开的腹部肿瘤分割数据集上进行了全面实验。结果表明，DEAP-3DSAM在3D图像分割中达到领先水平，优于或匹配现有依赖人工提示的方法。定量与定性消融实验均验证了所提模块的有效性。
原文摘要 · Abstract (English)
The Segment Anything Model (SAM) has recently demonstrated significant potential in medical image segmentation. Although SAM is primarily trained on 2D images, attempts have been made to apply it to 3D medical image segmentation. However, the pseudo 3D processing used to adapt SAM results in spatial feature loss, limiting its performance. Additionally, most SAM-based methods still rely on manual prompts, which are challenging to implement in real-world scenarios and require extensive external expert knowledge. To address these limitations, we introduce the Decoder Enhanced and Auto Prompt SAM (DEAP-3DSAM) to tackle these limitations. Specifically, we propose a Feature Enhanced Decoder that fuses the original image features with rich and detailed spatial information to enhance spatial features. We also design a Dual Attention Prompter to automatically obtain prompt information through Spatial Attention and Channel Attention. We conduct comprehensive experiments on four public abdominal tumor segmentation datasets. The results indicate that our DEAP-3DSAM achieves state-of-the-art performance in 3D image segmentation, outperforming or matching existing manual prompt methods. Furthermore, both quantitative and qualitative ablation studies confirm the effectiveness of our proposed modules.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。