Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This approach is very flexible and allows us to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g. Brownian) to another (e.g. a directed motion), along with the appearance over time of new trajectories and their death after some lifetime, all of these features possibly depending on the current spatial configuration of all existing particles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. Based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

翻译：生物成像中的时空动力学生成器已成为构建基准数据集（用于评估生物分子检测器与追踪器等图像处理算法）以及生成深度学习算法训练数据集的重要工具。本文利用一种名为"生-灭-移动"（BDM）点过程的随机模型，生成细胞中生物分子的联合动力学。该方法具有高度灵活性，可模拟运动粒子系统（可能包含相互作用），其中每个粒子可在不同运动模式（如布朗运动）之间切换（例如切换为定向运动），同时伴随新轨迹随时间出现及经过一定生命周期后消亡——所有这些特征可能取决于当前所有粒子的空间构型。我们通过多个与生物成像应用相关的实际案例，阐述了如何具体定义BDM模型的所有特征。最终基于真实荧光显微镜数据集，校准模型以模拟细胞膜附近朗格汉斯蛋白与Rab11蛋白的联合动力学。结果表明，生成的合成序列展现出与真实显微镜图像序列中可观测特征相媲美的特性。