迷失于体积：CT-SpatialVQA基准用于评估3D医学视觉-语言模型的语义-空间理解
Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
AI总结：
本文提出CT-SpatialVQA基准，评估3D医学视觉-语言模型对3DCT数据的语义-空间理解能力，发现现有模型在语义-空间推理任务中表现不佳，需更深入整合体积证据以确保临床可靠性。
摘要：
近年来，3D医学视觉-语言模型的进步使联合推理体积图像和文本成为可能，在医学视觉问答（VQA）和报告生成中表现出色。然而，尚不清楚这些模型是否从3D体积中学习了空间基础的解剖学，还是主要依赖于学习的先验知识和语言相关性。这种不确定性源于对体积医学VLMs语义-空间推理的系统评估不足。为此，我们引入CT-SpatialVQA基准，旨在评估3DCT数据的语义-空间推理能力。该基准包含9077个临床相关的问答对，直接来源于1601份放射学报告和CT体积，并通过稳健的LLM辅助流程验证，达到95%的人类共识一致率。我们的数据集要求显式解剖定位、左右意识、结构比较和3D结构间关系推理。我们还引入了标准化评估协议，并测试了八种3D医学VLMs，发现其在语义-空间推理任务中表现严重下降，平均准确率为34%，往往低于随机水平，凸显了在临床应用中更深入整合体积证据的必要性。
英文摘要：
Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical visual question-answering (VQA) and report generation. Despite this progress, it remains unclear whether these models learn spatially grounded anatomy from 3D volumes or rely primarily on learned priors and language correlations. This uncertainty stems from the lack of systematic evaluation of semantic-spatial reasoning in volumetric medical VLMs for clinically reliable decision support. To address this gap, we introduce CT-SpatialVQA, a benchmark designed to evaluate semantic-spatial reasoning in 3D CT data. The benchmark comprises 9077 clinically grounded question-answer (QA) pairs derived directly from 1601 radiology reports and CT volumes, which are validated via a robust LLM-assisted pipeline with a 95% human consensus agreement rate. Our dataset requires explicit anatomical localization, laterality awareness, structural comparison, and 3D inter-structure relational reasoning. We also introduce a standardized evaluation protocol and benchmark eight 3D medical VLMs, finding severe degradation on semantic-spatial reasoning tasks, averaging 34% accuracy and often below random, highlighting the need for deeper integration of volumetric evidence for trustworthy clinical use.
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