GLeVE: 在3D CT中基于图的病变接地与提案验证
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
AI总结：
本文提出GLeVE框架，通过图引导的病变接地和解剖学先验验证，解决3D CT中自由文本叙述与体积解剖之间的语义-空间差距问题，提升病变定位的准确性。
摘要：
将放射科报告描述接地到3D CT体积对于可验证的临床解释至关重要，但受到自由文本叙述与体积解剖之间语义-空间差距的挑战。现有基于报告辅助和视觉-语言接地的方法通常依赖于短语级对齐或密集像素监督，导致病变层面的对应有限和定位准确性不足。我们提出GLeVE，一种带有解剖学先验验证和基于八叉树的自回归细化的图引导病变接地框架。GLeVE将每个病变描述视为一个原子语义单元，并通过关系感知图推理编码器官归属、属性和跨病变关系，以生成具有判别性的病变层面查询。具有区域级验证的解剖学感知提案生成强制一对一的文本-病变对齐，而分层八叉树细化逐步改进边界界定。在AbdomenAtlas 3.0上的实验表明，GLeVE在分割准确性和病变层面定位方面均优于经典多模态基础模型和报告监督基线。
英文摘要：
Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-text narratives and volumetric anatomy. Existing report-assisted and vision-language grounding methods typically rely on phrase-level alignment or dense pixel supervision, resulting in limited lesion-wise correspondence and suboptimal localization accuracy. We propose GLeVE, a graph-guided lesion grounding framework with anatomical prior verification and octree-based autoregressive refinement. GLeVE treats each lesion description as an atomic semantic unit and encodes organ attribution, attributes, and inter-lesion relations through relation-aware graph reasoning to produce discriminative lesion-wise queries. Anatomy-aware proposal generation with region-level verification enforces one-to-one text-lesion alignment, while hierarchical octree refinement progressively improves boundary delineation. Experiments on AbdomenAtlas 3.0 demonstrate consistent gains over classical multimodal foundation models and report-supervised baselines in both segmentation accuracy and lesion-level localization.
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。
