BigReg：一种用于高分辨率X射线与光片荧光显微镜的高效配准流程
BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy
AI总结：
针对高分辨率XRM与LSFM体数据配准难题，提出两阶段快速配准流程BigReg，经测试精度接近界标配准，还能为互信息方法提供初始化以进一步提升精度，助力骨质疏松病理研究。
摘要：
近年来，X射线显微镜（XRM）和光片荧光显微镜（LSFM）已成为临床前研究中的关键工具，尤其适用于研究骨质疏松症等骨重塑疾病。这些模态可提供微米级分辨率，二者结合能够对骨微结构进行互补性检查，这对于分析功能变化至关重要。然而，配准来自这些独立扫描模态的高分辨率体数据面临巨大挑战，尤其是在真实场景和无参考场景下。本文提出BigReg，这是一种专为XRM和LSFM数据的大体量配准设计的快速两阶段流程。第一阶段包括提取表面特征，并应用两种连续的基于点云的方法进行粗对齐。后续阶段使用改进的互相关技术优化该对齐，实现精确的体配准。使用专家标注的界标和增强测试数据的评估表明，BigReg的配准精度接近基于界标的配准，其界标距离（LMD）为8.36 μm ± 0.12 μm，界标识配度（LM fitness）为85.71% ± 1.02%。此外，BigReg可为基于互信息的方法提供最优初始化——这类方法单独使用时会失效——进一步将LMD降至7.24 μm ± 0.11 μm，并将LM适配度提升至93.90% ± 0.77%。最终，关键微结构（尤其是XRM中的骨陷窝和LSFM中的骨细胞）得以精确对齐，为骨质疏松症的病理学研究提供了前所未有的见解。
英文摘要：
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling diseases such as osteoporosis. These modalities offer micrometer-level resolution, and their integration allows for a complementary examination of bone microstructures which is essential for analyzing functional changes. However, registering high-resolution volumes from these independently scanned modalities poses substantial challenges, especially in real-world and reference-free scenarios. This paper presents BigReg, a fast, two-stage pipeline designed for large-volume registration of XRM and LSFM data. The first stage involves extracting surface features and applying two successive point cloud-based methods for coarse alignment. The subsequent stage refines this alignment using a modified cross-correlation technique, achieving precise volumetric registration. Evaluations using expert-annotated landmarks and augmented test data demonstrate that BigReg approaches the accuracy of landmark-based registration with a landmark distance (LMD) of 8.36\,\textmu m\,$\pm$\,0.12\,\textmu m and a landmark fitness (LM fitness) of 85.71\%\,$\pm$\,1.02\%. Moreover, BigReg can provide an optimal initialization for mutual information-based methods which otherwise fail independently, further reducing LMD to 7.24\,\textmu m\,$\pm$\,0.11\,\textmu m and increasing LM fitness to 93.90\%\,$\pm$\,0.77\%. Ultimately, key microstructures, notably lacunae in XRM and bone cells in LSFM, are accurately aligned, enabling unprecedented insights into the pathology of osteoporosis.
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。
