In the biomedical environment, experiments assessing dynamic processes are
primarily performed by a human acquisition supervisor. Contemporary
implementations of such experiments frequently aim to acquire a maximum number
of relevant events from sometimes several hundred parallel, non-synchronous
processes. Since in some high-throughput experiments, only one or a few
instances of a given process can be observed simultaneously, a strategy for
planning and executing an efficient acquisition paradigm is essential. To
address this problem, we present two new methods in this paper. The first
method, Encoded Dynamic Process (EDP), is Artificial Intelligence (AI)-based
and represents dynamic processes so as to allow prediction of pseudo-time
values from single still images. Second, with Experiment Automation Pipeline
for Dynamic Processes (EAPDP), we present a Machine Learning Operations
(MLOps)-based pipeline that uses the extracted knowledge from EDP to
efficiently schedule acquisition in biomedical experiments for dynamic
processes in practice. In a first experiment, we show that the pre-trained
State-Of-The- Art (SOTA) object segmentation method Contour Proposal Networks
(CPN) works reliably as a module of EAPDP to extract the relevant object for
EDP from the acquired three-dimensional image stack.

在生物医学环境中，为了从多个非同步进程中获取尽可能多的相关事件，需要计划和执行一种高效的获取范式。为了解决这个问题，本文提出了两种新的方法：基于人工智能的编码动态进程方法（EDP），以及基于机器学习运营的动态进程实验自动化流水线（EAPDP），它使用从 EDP 中提取的知识来高效地调度实践中的生物医学实验的获取过程。在第一个实验中，我们展示了预训练的最先进的目标分割方法 CPN 作为 EAPDP 的一个模块，从获取的三维图像堆栈中提取出 EDP 相关的对象。