🤖 AI Summary
This work addresses the challenges of low reconstruction accuracy and poor generalization in 3D organ modeling from macroscopic slice imaging, which arise due to data scarcity and large deformations. To overcome these limitations, the authors propose a two-stage hybrid registration framework: an initial global rigid alignment is achieved through Optimal Slice Matching (OCM) combined with Hough transform, followed by local non-rigid deformation estimation using explicit geometric priors integrated into a lightweight, modified VoxelMorph network. By hierarchically decoupling global optimization from local refinement, the method significantly outperforms single-stage baselines—even when trained on only 40 kidney specimens—yielding more accurate, anatomically plausible, and reproducible multimodal 3D reconstructions suitable for surgical planning and medical education.
📝 Abstract
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.