基于深度学习的3D医学重建在计算机断层扫描和宏观成像中的校正算法
Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imaging
AI总结：
本文提出了一种混合两阶段配准框架，结合几何先验和深度学习，提升3D医学重建的精度和解剖真实感。
摘要：
本文介绍了一种混合的两阶段配准框架，用于从宏观切片中重建三维（3D）肾脏解剖结构，使用CT衍生模型作为几何参考标准。该方法解决了宏观成像中常见的数据稀缺和高畸变挑战，其中完全基于学习的配准（如VoxelMorph）由于训练多样性有限和大非刚性变形超过无约束卷积滤波器的捕捉范围而往往无法泛化。在所提出的流程中，最优切片匹配（OCM）算法首先执行受约束的全局对齐：平移、旋转和均匀缩放，以建立解剖上一致的切片初始化。接下来，一个轻量级的深度学习细化网络，受VoxelMorph启发，预测连续切片之间的残余局部变形。该架构的核心创新点在于其对配准流形的分层分解。这种混合OCM+DL设计将显式的几何先验与神经网络的灵活学习能力相结合，即使在少量训练示例的情况下也能确保稳定的优化和合理的变形场。在原始数据集上进行的实验表明，与单阶段基线相比，该方法取得了更好的结果。该流程通过Hough基于的网格检测保持物理校准，并采用贝塞尔基于的轮廓平滑进行稳健的网格化和体积估计。虽然验证了肾脏数据，但所提出的框架可以推广到其他软组织器官的重建，这些器官是从光学或摄影切片中重建的。通过将可解释的全局优化与数据高效的深度细化解耦，该方法提高了多模态3D重建的精度、可重复性和解剖真实感，用于手术规划、形态学评估和医学教育。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
