MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.
MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.
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使用空间和形状信息以及分支定界算法的多模态显微图像对齐。
DOI：
10.1109/icassp49357.2023.10096185
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Varol,Erdem
中科院分区：
文献类型：
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作者：
Chen,Shuonan;Rao,BoveyY;Herrlinger,Stephanie;Losonczy,Attila;Paninski,Liam;Varol,Erdem
在不同实验条件下对相同细胞群进行成像的多模态显微镜实验已成为系统和分子神经科学中广泛使用的方法。主要障碍是对准不同的成像模态以获得关于观察到的细胞群体的补充信息（例如，基因表达和钙信号）。传统的图像配准方法表现不佳时，只有一小部分细胞存在于两个图像中，这是常见的多模态实验。我们铸造多模态显微镜对齐作为一个细胞子集匹配问题。为了解决这个非凸问题，我们引入了一个有效的和全局最优的分支定界算法来找到彼此旋转对齐的点云子集。此外，我们使用细胞形状和位置的互补信息来计算两种成像模式中细胞对的匹配可能性，以进一步修剪优化搜索树。最后，我们使用刚性旋转对齐中的最大单元集来种子图像变形场，以获得最终的配准结果。我们的框架在匹配质量方面优于最先进的组织学对齐方法，并且比手动对齐更快，为提高多模态显微镜实验的吞吐量提供了可行的解决方案。
Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.