GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT

• URL: http://arxiv.org/abs/2605.22619v1
• Date: Thu, 21 May 2026 15:30:41 GMT
• Title: GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
• Abstract summary: Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation.<n>Existing report-assisted and vision-language grounding methods rely on phrase-level alignment or dense pixel supervision.<n>We propose GLeVE, a graph-guided lesion grounding framework with anatomical prior verification and octree-based autoregressive refinement.
• Score: 16.979389238358937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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