发表机构
Polytechnique Montréal; Mila - Quebec AI Institute; CHUM - University of Montreal Hospital（蒙特利尔理工学院; 米拉-魁北克人工智能研究所; 蒙特利尔大学附属CHUM医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对X光片三维重建的不适定问题，提出联合2D-3D统计形状模型，在共享潜空间捕获二维与三维协变关系，直接学习映射，无需迭代投影，在NMDID上精度更优且推理快约4倍。
AI 中文摘要
从X光片进行三维股骨重建支持手术规划、植入物尺寸选择和术后随访，但由于X射线投影丢失深度信息，这一问题仍是不适定的。现有方法通常将三维统计形状模型（SSM）作为形状先验，通过迭代的三维到二维投影匹配来引导重建朝向解剖学合理的形状。然而，这些方法计算成本高昂，且将其SSM限制在单一维度，导致二维观测与三维几何之间的统计关系在很大程度上未被利用和探索。我们转而提出一种联合2D-3D SSM，在共享潜空间中显式捕获二维和三维分割之间的协变关系。在训练期间，二维和三维分割被配准到共同的二维模板及其对应的二维投影，所得平稳速度场使用主成分分析（PCA）进行联合分解。这种联合建模允许直接从数据中学习二维到三维的映射，而非在推理时计算对应关系。对于未见过的受试者，通过将二维潜在坐标提升到三维PCA子空间直接恢复三维形状，从而消除了迭代三维到二维投影的需要。在NMDID上的实验表明，所提出的联合2D-3D SSM优于广泛使用的仅三维SSM基线，同时推理速度约为其4倍，每个受试者耗时低于3秒。代码可在以下网址获取：this https URL。
英文摘要
Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections discard depth information. Existing methods often incorporate a 3D statistical shape model (SSM) as a shape prior to guide reconstructions toward anatomically plausible shapes, relying on iterative 3D-to-2D projection matching. Yet, these approaches are computationally expensive and constrain their SSM to a single dimensionality, leaving the statistical relationship between 2D observations and 3D geometry largely unexploited and unexplored. We instead propose a joint 2D-3D SSM that explicitly captures the co-variation between 2D and 3D segmentations in a shared latent space. During training, 2D and 3D segmentations are registered to a common 3D template and its corresponding 2D projections, and the resulting stationary velocity fields are jointly decomposed using principal component analysis (PCA). This joint modeling allows the 2D-to-3D mapping to be learned directly from data rather than computing correspondences at inference time. For unseen subjects, the 3D shape is recovered directly by lifting the 2D latent coordinates to the 3D PCA subspace, thereby eliminating the need for iterative 3D-to-2D projection. Experiments on NMDID demonstrate that the proposed joint 2D-3D SSM outperforms a widely-used 3D-only SSM baseline while achieving inference approximately 4 times faster, at under 3 seconds per subject. The code is available at: https://github.com/florence-dellaniello-picard/joint2d3d-ssm.
CommentsAccepted to MICCAI Workshop on Shape in Medical Imaging (ShapeMI)
