Building patient-specific cardiac models sits at the heart of precision cardiology, yet getting those models into clinical use keeps running into the same wall: mesh generation is slow, messy, and frustrating. The standard workflow -- segmenting the image, running Marching Cubes, and then manually cleaning up the result -- is time-consuming, inconsistent across operators, and demands specialist knowledge most clinical teams do not have. We take a fundamentally different approach. Instead of treating segmentation and mesh generation as two separate problems, we train a single end-to-end network that goes directly from a raw 3D medical image to a smooth, simulation-ready cardiac surface mesh. The core is a 3D Swin Transformer encoder-decoder that extracts volumetric features from CT or MRI volumes, paired with a Graph Attention Network (GAT) head that iteratively deforms a template mesh to fit the patient's cardiac boundary. We tested on the MM-WHS 2017 benchmark using both CT and MRI. Segmentation scores were competitive (Dice of 0.84 on CT, 0.83 on MRI), but the primary focus is mesh quality: mean Chamfer distance of 1.8 mm, with 95th-percentile surface distance below 5 mm. Every mesh is produced in a single forward pass -- no Marching Cubes, no smoothing filters, no manual cleanup. We argue that for cardiac digital twin pipelines, geometric fidelity and topological correctness matter more than pixel-level Dice scores. By removing the post-processing bottleneck, this approach makes patient-specific cardiac simulation substantially more accessible for clinical use.

翻译：构建患者特异性心脏模型是精准心脏病学的核心，但将这些模型投入临床始终面临同一瓶颈：网格生成过程缓慢、繁琐且令人困扰。标准工作流——图像分割、运行Marching Cubes算法、再手动清理结果——不仅耗时、操作者间一致性差，更要求大多数临床团队不具备的专业知识。我们提出一种根本性不同的方法：不将分割与网格生成视为两个独立问题，而是训练单个端到端网络，直接从原始三维医学图像生成光滑且可用于仿真的心脏表面网格。其核心是3D Swin Transformer编码器-解码器，用于从CT或MRI容积中提取体素特征，并配合图注意力网络（GAT）头部，通过迭代变形模板网格以拟合患者心脏边界。我们在MM-WHS 2017基准上使用CT和MRI进行测试。分割得分具有竞争力（CT上Dice系数0.84，MRI上0.83），但主要关注点在于网格质量：平均Chamfer距离1.8 mm，95百分位表面距离低于5 mm。所有网格均可通过单次前向传播生成——无需Marching Cubes、无需平滑滤波器、无需手动清理。我们认为，对于心脏数字孪生管线而言，几何保真度和拓扑正确性比像素级Dice系数更为重要。通过消除后处理瓶颈，该方法使患者特异性心脏仿真在临床应用中更易实现。