[论文] DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Fre...

论文概要

研究领域: CV 作者: Tal Grossman, Noa Cahan, Lev Ayzenberg 发布时间: 2025-04-29 arXiv: 2504.20597

中文摘要

DiffuSAM 是SAM2的基于扩散的适配框架，用于免提示医学图像分割。该框架通过轻量级扩散先验从现成的冻结SAM2图像特征合成SAM2兼容的分割掩码嵌入，生成的嵌入集成到SAM2的掩码解码器中产生准确分割，消除了对用户提示的需求。扩散先验进一步以先前分割的切片为条件，强制执行跨体积的空间一致性。在BTCV和CHAOS数据集的CT和MRI上评估，在SF-UDA和少样本设置下实现了具有竞争力的性能。

原文摘要

Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transfer to medical data. Consequently, accurate segmentation typically requires extensive fine-tuning and expert-designed prompts. We propose DiffuSAM, a diffusion-based adaptation of SAM2 for prompt-free medical image segmentation. Our framework synthesizes SAM2-compatible segmentation mask-like embeddings via a lightweight diffusion-prior from off-the-shelf frozen SAM2 image features. The generated embeddings are integrated into SAM2's mask decoder to produce accurate segmentations, thereby eliminating the need for user prompts. The diffusion prior is further conditioned on previously segmented slices, enforcing spa...

自动采集于 2026-04-29

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