SurgFormer: 可扩展的器官形变学习与切除支持及实时推断
SurgFormer: Scalable Learning of Organ Deformation with Resection Support and Real-Time Inference
AI总结：
SurgFormer通过多分辨率门控变压器实现高效体积网格软组织模拟，结合切除信息与实时推断，提升手术模拟的准确性和效率。
摘要：
我们介绍了SurgFormer，一种多分辨率门控变压器，用于数据驱动的体积网格软组织模拟。高保真的生物力学求解器通常在交互使用时成本过高，因此我们训练SurgFormer在求解器生成的数据上，以在接近实时速率预测节点级位移场。SurgFormer构建了一个固定的网格层次结构，并应用了重复的多分支块，这些块结合了局部消息传递、粗粒度全局自注意力和点wise前馈更新，通过学习的每节点、每通道门融合，以适应性地整合局部和长距离信息，同时在大网格上保持可扩展性。对于切条件模拟，切除信息被编码为一个学习的切嵌入，并作为额外输入提供，使模型能够统一处理标准变形预测和拓扑改变情况。我们还介绍了两个手术模拟数据集，这些数据集是在统一协议下，基于XFE M监督生成的：胆囊切除切除数据集和阑尾切除操作和切除数据集，包含切与未切情况。据我们所知，这是第一个学习体积替代设置，研究XFE M监督的切条件变形，同时在与标准变形预测相同的体积管道中。在多样化的基线模型上，SurgFormer实现了高精度和良好的效率，使其成为两项任务的实用骨干。{代码、数据和项目页面：\href{https://mint-vu.github.io/SurgFormer/}{此处可用}}
英文摘要：
We introduce SurgFormer, a multiresolution gated transformer for data driven soft tissue simulation on volumetric meshes. High fidelity biomechanical solvers are often too costly for interactive use, so we train SurgFormer on solver generated data to predict nodewise displacement fields at near real time rates. SurgFormer builds a fixed mesh hierarchy and applies repeated multibranch blocks that combine local message passing, coarse global self attention, and pointwise feedforward updates, fused by learned per node, per channel gates to adaptively integrate local and long range information while remaining scalable on large meshes. For cut conditioned simulation, resection information is encoded as a learned cut embedding and provided as an additional input, enabling a unified model for both standard deformation prediction and topology altering cases. We also introduce two surgical simulation datasets generated under a unified protocol with XFEM based supervision: a cholecystectomy resection dataset and an appendectomy manipulation and resection dataset with cut and uncut cases. To our knowledge, this is the first learned volumetric surrogate setting to study XFEM supervised cut conditioned deformation within the same volumetric pipeline as standard deformation prediction. Across diverse baselines, SurgFormer achieves strong accuracy with favorable efficiency, making it a practical backbone for both tasks. {Code, data, and project page: \href{https://mint-vu.github.io/SurgFormer/}{available here}}
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。
