图谱是理想上下文：用于可泛化基础医学图像分割的一次性定制
Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation
AI总结：
研究医学图像中解剖结构分割问题，提出图谱引导框架AtlasSegFM，通过图谱查询配准生成提示，用冻结基础模型细化分割，经自适应融合模块结合图谱先验与模型输入及预测，实现一次性定制。
摘要：
医学图像中解剖结构的准确分割对诊断和治疗规划至关重要。虽然最近的交互式分割基础模型通过大规模多模态预训练增强了泛化能力，但它们仍依赖精确提示，在代表性不足的临床环境中可能失败。我们提出了AtlasSegFM，这是一个图谱引导的框架，通过单个带注释的示例将现成的基础模型定制到新的临床环境中。AtlasSegFM 1) 执行图谱查询配准以生成上下文感知提示，2) 用冻结的基础模型细化分割，3) 应用轻量级自适应融合模块将图谱先验与基础模型输入和预测相结合。在放疗和血管场景的六个公共和内部数据集上进行的广泛实验显示出一致的收益，对小而精细的结构有最大的改进。AtlasSegFM为在实际临床工作流程中对分割基础模型进行一次性定制提供了一个轻量级、可部署的解决方案。
英文摘要：
Accurate segmentation of anatomical structures in medical images is essential for diagnosis and treatment planning. While recent interactive segmentation foundation models enhance generalization through large-scale multimodal pretraining, they still depend on precise prompts and can fail in underrepresented clinical contexts (e.g., small organs-at-risk). We present AtlasSegFM, an atlas-guided framework that customizes off-the-shelf foundation models to new clinical contexts with a single annotated example. AtlasSegFM 1) performs atlas-query registration to generate context-aware prompts, 2) refines the segmentation with a frozen foundation model, and 3) applies a lightweight adaptive fusion module to combine atlas priors with foundation-model inputs and predictions. Extensive experiments on six public and in-house datasets across radiotherapy and vascular scenarios show consistent gains, with the largest improvements on small and delicate structures. AtlasSegFM provides a lightweight, deployable solution for one-shot customization of segmentation foundation models in real-world clinical workflows.
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