Du Tiehua, Wasser Martin
Bioinformatics Institute (BII), Agency for Science, Technology and Research, (A*STAR), Singapore.
Cytometry A. 2009 Apr;75(4):329-43. doi: 10.1002/cyto.a.20701.
Rapid movements of live tissues during the acquisition of 3D image stacks can result in misalignments between successive image slices. The remodeling of the muscles in Drosophila metamorphosis is an example where sporadic motion during image acquisition impede image analysis and volume visualization. Most of the image stack registration algorithms applied in microscopy are aimed at the linear alignment of fixed histological sections. However, live muscles are nonrigid objects and their contractions and relaxations represent nonlinear transformations that cannot be properly rectified by applying purely linear registration methods. We developed a fully automated area-based nonrigid stack registration (NSR) method that minimizes the mean square error of intensities between successive image slices. The mapping function is formulated using the thin plate spline (TPS). A hierarchical linear to nonlinear, coarse to fine matching strategy is applied to ensure stability and fast convergence. Topological structure is preserved by constraining the step size of the nonlinear transformation. To assess the accuracy of 3D reconstruction, we propose a new benchmarking method that measures geometrical features of restored nuclei. We tested our algorithm on image stacks generated by laser scanning confocal microscopy that show live muscles during the prepupal stage of Drosophila metamorphosis. Our registration algorithm is able to restore image stacks that are distorted by periodic contraction of muscles. Quantitative assessment of registration performance agrees well with qualitative visual inspection. Our NSR method is able to restore image stacks for the purpose of visualization and quantitative analysis of Drosophila metamorphosis and, potentially, various other processes in developmental biology studied by 3D live cell microscopy.
在获取三维图像堆栈过程中，活组织的快速运动可能导致连续图像切片之间出现错位。果蝇变态过程中肌肉的重塑就是一个例子，图像采集过程中的零星运动会妨碍图像分析和体积可视化。显微镜学中应用的大多数图像堆栈配准算法都旨在对固定组织切片进行线性对齐。然而，活体肌肉是非刚性物体，它们的收缩和舒张代表非线性变换，单纯应用线性配准方法无法对其进行适当校正。我们开发了一种基于区域的全自动非刚性堆栈配准（NSR）方法，该方法可使连续图像切片之间强度的均方误差最小化。映射函数使用薄板样条（TPS）来制定。应用了从线性到非线性、从粗到细的分层匹配策略，以确保稳定性和快速收敛。通过约束非线性变换的步长来保留拓扑结构。为了评估三维重建的准确性，我们提出了一种新的基准测试方法，该方法可测量恢复细胞核的几何特征。我们在激光扫描共聚焦显微镜生成的图像堆栈上测试了我们的算法，这些图像堆栈展示了果蝇变态预蛹期的活体肌肉。我们的配准算法能够恢复因肌肉周期性收缩而扭曲的图像堆栈。配准性能的定量评估与定性视觉检查结果吻合良好。我们的NSR方法能够恢复图像堆栈，用于果蝇变态以及潜在地用于三维活细胞显微镜研究的发育生物学中各种其他过程的可视化和定量分析。
