VoxelSage：面向肝脏肿瘤的基于工具增强的3D CT分析与模拟器防护的序贯切除规划
VoxelSage: Tool-Augmented 3D CT Analysis and Simulator-Shielded Sequential Resection Planning for Liver Tumors
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中文总结 AI 辅助
VoxelSage提出一种双端口架构的多模态系统，集成3D CT分割、测量与序贯切除规划，通过模拟器防护和神经排序器优化，显著减少模拟手术时间和失血。
中文摘要 AI 辅助
术前肝脏肿瘤评估需要对同一三维CT体积进行分割、物理空间测量、视觉证据生成和切除规划。现有工具通常分别处理这些步骤，而语言模型无法可靠地从CT计算物理测量值。为提供集成工作流，我们提出了VoxelSage，一个用于二维和三维可视化、肝脏肿瘤分析和术前切除规划的多模态系统。其双端口架构将语言模型编排与图像计算分离：端口A解释请求并选择技能，而端口B将这些技能应用于CT体积和分割掩膜，并返回结构化结果。将物理测量保持在端口B中可防止LLM直接计算它们，并降低生成虚构数值结果的风险。八个内置技能支持定量分析、视觉证据生成、三维重建、分割细化和序贯切除规划；用户自定义技能可扩展这些功能。对于序列规划，一个行为克隆的神经排序器对候选切除目标进行排序，而基于模拟器的防护根据预定义约束检查它们。在256个未见过的模拟器场景中，相对于确定性基线，该方法将平均模拟时间从34.274分钟减少到33.388分钟（减少0.886分钟，2.59%），平均模拟失血量从300.847毫升减少到183.852毫升（减少116.995毫升，38.89%）。这些结果证明了系统集成和模拟器层面的性能，而非临床疗效或安全性。公开实现可在https URL获取。
英文摘要
Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dimensional CT volume. Existing tools often handle these steps separately, while language models cannot reliably compute physical measurements from CT. To provide an integrated workflow, we present VoxelSage, a multi-modal system for two- and three-dimensional visualization, liver-tumor analysis, and preoperative resection planning. Its dual-port architecture separates language-model orchestration from image computation: Port A interprets requests and selects skills, while Port B applies them to CT volumes and segmentation masks and returns structured results. Keeping physical measurements in Port B prevents the LLM from computing them directly and reduces the risk of fabricated numerical results. Eight built-in skills support quantitative analysis, visual evidence generation, three-dimensional reconstruction, segmentation refinement, and sequential resection planning; user-defined skills can extend these functions. For sequence planning, a behavior-cloned neural ranker orders candidate resection targets, while a simulator-based shield checks them against predefined constraints. Across 256 unseen simulator scenes, this approach reduced mean simulated time from 34.274 to 33.388 min (0.886 min, 2.59%) and mean simulated blood loss from 300.847 to 183.852 mL (116.995 mL, 38.89%) relative to a deterministic baseline. These results demonstrate system integration and simulator-level performance, not clinical efficacy or safety. The public implementation is available at https://github.com/ZJUMAI/VoxelSage.
发表机构
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
