论文标题
通过贝叶斯数据选择未标记的地标匹配，并在成像方式上应用于细胞匹配
Unlabelled landmark matching via Bayesian data selection, and application to cell matching across imaging modalities
论文作者
论文摘要
我们考虑了两个未标记点集之间的地标匹配问题，特别是每个云中的点数可能有所不同，而每个云中的点可能没有相应的匹配。我们调用一个贝叶斯框架，以确定将一个云映射到另一个云的坐标的转换，并与点的对应关系。这个问题需要一种新的贝叶斯数据选择方法。同时推断模型参数以及选择数据最佳拟合数据的数据。我们将其应用于发育生物学中的问题，其中地标对应于分段的细胞中心，在该中心中，潜在的死亡或细胞分裂可能导致点集与每个图像之间的差异。我们在硅测试中验证方法的疗效和微注射的荧光标记实验。随后，我们将方法应用于实时成像和免疫染色实验之间的细胞匹配，从而促进了成像方式之间单细胞数据的组合。此外，我们对贝叶斯数据选择的方法在数据科学之间广泛适用，并且有可能改变我们考虑将模型拟合到数据的方式。
We consider the problem of landmark matching between two unlabelled point sets, in particular where the number of points in each cloud may differ, and where points in each cloud may not have a corresponding match. We invoke a Bayesian framework to identify the transformation of coordinates that maps one cloud to the other, alongside correspondence of the points. This problem necessitates a novel methodology for Bayesian data selection; simultaneous inference of model parameters, and selection of the data which leads to the best fit of the model to the majority of the data. We apply this to a problem in developmental biology where the landmarks correspond to segmented cell centres, where potential death or division of cells can lead to discrepancies between the point-sets from each image. We validate the efficacy of our approach using in silico tests and a microinjected fluorescent marker experiment. Subsequently we apply our approach to the matching of cells between real time imaging and immunostaining experiments, facilitating the combination of single-cell data between imaging modalities. Furthermore our approach to Bayesian data selection is broadly applicable across data science, and has the potential to change the way we think about fitting models to data.
