Journal of medical imaging (Bellingham, Wash.) · 2025
BigReg: an efficient registration pipeline for high-resolution X-ray and light-sheet fluorescence microscopy.
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Mei Siyuan, Fan Fuxin, Thies Mareike, Gu Mingxuan, Wagner Fabian, Aust Oliver, Erceg Ina, Mirzaei Zeynab, Neag Georgiana, Sun Yipeng, Huang Yixing, Maier Andreas
Аннотация
PURPOSE: We aim to propose a reliable registration pipeline tailored for multimodal mouse bone imaging using X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM). These imaging modalities have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling diseases such as osteoporosis. Although multimodal registration enables micrometer-level structural correspondence and facilitates functional analysis, conventional landmark-, feature-, or intensity-based approaches are often infeasible due to inconsistent signal characteristics and significant misalignment resulting from independent scanning, especially in real-world and reference-free scenarios.
APPROACH: To address these challenges, we introduce BigReg, an automatic, two-stage registration pipeline optimized for high-resolution XRM and LSFM volumes. The first stage involves extracting surface features and applying two successive global-to-local point-cloud-based methods for coarse alignment. The subsequent stage refines this alignment in the 3D Fourier domain using a modified cross-correlation technique, achieving precise volumetric registration.
RESULTS: 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 ± 0.12 μ m and a landmark fitness (LM fitness) of 85.71 % ± 1.02 % . Moreover, BigReg can provide an optimal initialization for mutual information-based methods that otherwise fail independently, further reducing LMD to 7.24 ± 0.11 μ m and increasing LM fitness to 93.90 % ± 0.77 % .
CONCLUSIONS: To the best of our knowledge, BigReg is the first automated method to successfully register XRM and LSFM volumes without requiring manual intervention or prior alignment cues. Its ability to accurately align fine-scale structures, such as lacunae in XRM and osteocytes in LSFM, opens up new avenues for quantitative, multimodal analysis of bone microarchitecture and disease pathology, particularly in studies of osteoporosis.
APPROACH: To address these challenges, we introduce BigReg, an automatic, two-stage registration pipeline optimized for high-resolution XRM and LSFM volumes. The first stage involves extracting surface features and applying two successive global-to-local point-cloud-based methods for coarse alignment. The subsequent stage refines this alignment in the 3D Fourier domain using a modified cross-correlation technique, achieving precise volumetric registration.
RESULTS: 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 ± 0.12 μ m and a landmark fitness (LM fitness) of 85.71 % ± 1.02 % . Moreover, BigReg can provide an optimal initialization for mutual information-based methods that otherwise fail independently, further reducing LMD to 7.24 ± 0.11 μ m and increasing LM fitness to 93.90 % ± 0.77 % .
CONCLUSIONS: To the best of our knowledge, BigReg is the first automated method to successfully register XRM and LSFM volumes without requiring manual intervention or prior alignment cues. Its ability to accurately align fine-scale structures, such as lacunae in XRM and osteocytes in LSFM, opens up new avenues for quantitative, multimodal analysis of bone microarchitecture and disease pathology, particularly in studies of osteoporosis.