发表机构
China Electronics (Beijing) Information Technology Research Institute Co., Ltd.（中国电子（北京）信息技术研究院有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MR-RS-SDFR框架，利用MRI边缘场和重切片一致性细化跨切片SDF，在稀疏轴向监督下重建左心室表面，实验显示优于现有方法。
AI 中文摘要
当仅在小数量轴向切片上提供监督时，从心脏磁共振（CMR）数据重建三维左心室（LV）心内膜表面具有挑战性。平面外几何约束较弱，自动生成的二维掩膜可能将分割误差传播到恢复的形状中。我们提出了MR-RS-SDFR，一种逐病例隐式符号距离场（SDF）框架，从CMR体积和稀疏轴向弱掩膜重建连续的LV表面。该方法首先从轴向和纵向几何线索构建跨切片SDF初始化，然后使用两个互补信号细化场：MRI边缘场法线对齐，提供独立于弱掩膜的图像衍生边界线索，以及可微分的重切片Dice和轮廓一致性，保持与观测平面的一致性。我们评估了三种弱掩膜生成器——LOO TransUNet、LOO nnU-Net和未经MM-WHS特定训练或微调的现成Medical SAM3模型——以及从4到64个轴向平面的五种稀疏度水平。在稀疏-16设置中，最终的MR-RS-SDFR重建使用Medical SAM3掩膜达到0.928 Dice和3.80mm HD95。上游生成器在2D和密集3D分割中未表现出单一共同排名，nnU-Net和Medical-SAM3驱动的稀疏重建尽管上游误差分布不同，但达到相同的平均最终Dice。在所有三种稀疏-16掩膜源中，MR-RS-SDFR在Dice和HD95上均数值优于协议匹配的完整GHD+DVS。最终Dice从稀疏-4到稀疏-16显著改善，然后在稀疏-64时达到报告精度并饱和。这些结果支持MRI引导的逐病例SDF细化作为一种重建策略，在弱掩膜生成器和监督密度下保持有效。
英文摘要
Reconstructing a three-dimensional left-ventricular (LV) endocardial surface from cardiac magnetic resonance (CMR) data is challenging when supervision is available on only a small number of axial slices. Through-plane geometry is weakly constrained, and automatically generated two-dimensional masks can propagate segmentation errors into the recovered shape. We present MR-RS-SDFR, a per-case implicit signed distance field (SDF) framework that reconstructs a continuous LV surface from a CMR volume and sparse axial weak masks. The method first builds a cross-slice SDF initialization from axial and longitudinal geometric cues and then refines the field using two complementary signals: MRI edge-field normal alignment, which provides an image-derived boundary cue independent of the weak masks, and differentiable reslice Dice and contour consistency, which preserve agreement with the observed planes. We evaluate three weak-mask generators -- LOO TransUNet, LOO nnU-Net, and an off-the-shelf Medical SAM3 model used without MM-WHS-specific training or fine-tuning -- and five sparsity levels from 4 to 64 axial planes. In the sparse-16 setting, final MR-RS-SDFR reconstruction reaches 0.928 Dice and 3.80mm HD95 with Medical SAM3 masks. The upstream generators do not exhibit a single common ranking across 2D and dense 3D segmentation, and nnU-Net- and Medical-SAM3-driven sparse reconstruction achieve the same mean final Dice despite different upstream error profiles. Across all three sparse-16 mask sources, MR-RS-SDFR is numerically better than protocol-matched full GHD+DVS in both Dice and HD95. Final Dice improves markedly from sparse-4 to sparse-16 and then saturates at the reported precision through sparse-64. These results support MRI-guided per-case SDF refinement as a reconstruction strategy that remains effective across weak-mask generators and supervision densities.
