Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis. However, while previous studies have focused primarily on either image segmentation or downstream use of 3D models, the reconstruction of high-fidelity, high-quality geometries remains labor-intensive and poorly standardized. The study introduced a hybrid deformable shape modeling framework that integrates deep learning prediction with iterative optimization for the reconstruction of the bladder, uterus, and rectum. The framework consists of three core components: a geometry-aware multi-level deep learning architecture that preserves topological consistency of pelvic organs; a two-stage amortized optimization training strategy that balances global shape capture and local surface refinement; and a holistic synergy mechanism--where iterative optimization provides supervision for deep learning during the training phase, and during inference, deep learning rapidly predicts the global organ morphology, followed by iterative optimization to refine local surfaces and mesh quality. This framework demonstrated marked superiority in geometric fidelity than current mainstream deep learning-based organ reconstruction models. For individual anatomical structures, the reconstructed 3D geometries for the bladder, rectum, and uterus achieved significantly lower Chamfer Distance values and higher Dice Similarity Coefficient scores. In addition, while maintaining high computational efficiency, the proposed architecture yielded superior overall volumetric mesh quality. At the patient level, the framework achieved higher mean values for the 10 worst elements for both minSICN and minSIGE compared to traditional geometric post-processing algorithms.

翻译：从磁共振成像中对患者特异性盆腔器官几何形状进行三维重建对于盆底建模及下游患者特异性分析至关重要。然而，以往研究主要聚焦于图像分割或三维模型的下游应用，高保真、高质量几何形状的重建仍存在劳动密集且标准化程度低的问题。本研究提出了一种混合变形形状建模框架，该框架整合了深度学习预测与迭代优化，用于膀胱、子宫和直肠的重建。该框架包含三个核心组件：一种保持盆腔器官拓扑一致性的几何感知多层级深度学习架构；一种平衡全局形状捕捉与局部表面细化的两阶段摊销优化训练策略；以及一种整体协同机制——在训练阶段，迭代优化为深度学习提供监督；在推理阶段，深度学习快速预测全局器官形态，随后通过迭代优化细化局部表面和网格质量。该框架在几何保真度上显著优于当前主流的基于深度学习的器官重建模型。对于各解剖结构，重建的膀胱、直肠和子宫三维几何形状实现了更低的倒角距离值和更高的Dice相似系数得分。此外，在保持高计算效率的同时，所提架构生成了更优的整体体积网格质量。在患者层面，与传统的几何后处理算法相比，该框架在最小内切圆归一化系数和最小内角归一化系数的前十最差单元上均获得了更高的平均值。