提出新型可微分胎儿形体模型，提升医学影像中关节结构分析的准确性。
Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms
- 基于骨骼树的对数欧几里得多刚体变换，解决大范围运动下的歧义问题。
- 在53个胎儿MRI上减少形变场折叠伪影，提升体积映射平滑性。
- 适合需要高精度形体建模的胎儿影像分析与少样本器官分割任务。
自动化分析可动人体在医学影像中至关重要。现有基于表面的模型常忽略内部体积结构，且依赖缺乏解剖一致性保证的变形方法。为此，我们提出一种基于SMPL的可微分体积化身体模型，采用新的基于骨骼树的对数欧几里得多刚体（KTPolyRigid）变换。KTPolyRigid解决了大范围非局部运动带来的李代数歧义问题，并促进平滑、双射的体积映射。在53个胎儿MRI数据上评估显示，该方法显著减少了形变场中的折叠伪影。此外，该框架实现了稳健的组间图像配准和标签效率高的模板式胎儿器官分割，为医学影像中可动体的标准化体积分析提供了坚实基础。
原文摘要 · Abstract (English)
Automated analysis of articulated bodies is crucial in medical imaging. Existing surface-based models often ignore internal volumetric structures and rely on deformation methods that lack anatomical consistency guarantees. To address this problem, we introduce a differentiable volumetric body model based on the Skinned Multi-Person Linear (SMPL) formulation, driven by a new Kinematic Tree-based Log-Euclidean PolyRigid (KTPolyRigid) transform. KTPolyRigid resolves Lie algebra ambiguities associated with large, non-local articulated motions, and encourages smooth, bijective volumetric mappings. Evaluated on 53 fetal MRI volumes, KTPolyRigid yields deformation fields with significantly fewer folding artifacts. Furthermore, our framework enables robust groupwise image registration and a label-efficient, template-based segmentation of fetal organs. It provides a robust foundation for standardized volumetric analysis of articulated bodies in medical imaging.
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