This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging, where fully learning-based registration (e.g., VoxelMorph) often fails to generalize due to limited training diversity and large nonrigid deformations that exceed the capture range of unconstrained convolutional filters. In the proposed pipeline, the Optimal Cross-section Matching (OCM) algorithm first performs constrained global alignment: translation, rotation, and uniform scaling to establish anatomically consistent slice initialization. Next, a lightweight deep-learning refinement network, inspired by VoxelMorph, predicts residual local deformations between consecutive slices. The core novelty of this architecture lies in its hierarchical decomposition of the registration manifold. This hybrid OCM+DL design integrates explicit geometric priors with the flexible learning capacity of neural networks, ensuring stable optimization and plausible deformation fields even with few training examples. Experiments on an original dataset of 40 kidneys demonstrated better results compared to single-stage baselines. The pipeline maintains physical calibration via Hough-based grid detection and employs Bezier-based contour smoothing for robust meshing and volume estimation. Although validated on kidney data, the proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections. By decoupling interpretable global optimization from data-efficient deep refinement, the method advances the precision, reproducibility, and anatomical realism of multimodal 3D reconstructions for surgical planning, morphological assessment, and medical education.

翻译：本文提出了一种混合两阶段配准框架，用于从宏观切片重建三维肾脏解剖结构，并以CT衍生模型作为几何参考标准。该方法解决了宏观成像中典型的数据稀缺和高畸变挑战，其中完全基于学习的配准方法（如VoxelMorph）由于训练数据多样性有限以及超出无约束卷积滤波器捕获范围的大规模非刚性形变，往往难以泛化。在所提出的流程中，最优横截面匹配算法首先执行约束全局对齐：通过平移、旋转和均匀缩放建立解剖学一致的切片初始化。随后，一个受VoxelMorph启发的轻量级深度学习优化网络预测连续切片间的残余局部形变。该架构的核心创新在于对配准流形的层次化分解。这种混合设计将显式几何先验与神经网络的灵活学习能力相结合，即使在训练样本稀少的情况下也能确保稳定的优化和合理的形变场。在包含40个肾脏的原始数据集上的实验表明，相较于单阶段基线方法，本方法取得了更优的结果。该流程通过基于霍夫变换的网格检测保持物理校准，并采用基于贝塞尔曲线的轮廓平滑技术实现鲁棒的网格生成和体积估计。虽然本框架在肾脏数据上得到验证，但其可泛化至其他通过光学或摄影横截面重建的软组织器官。通过将可解释的全局优化与数据高效的深度优化解耦，本方法提升了多模态三维重建在外科规划、形态学评估和医学教育中的精度、可重复性和解剖真实性。