Biological tissues are often dynamic and highly organized. Its Spatio-temporal patterns are relevant in many biological processes such as tissue homeostasis, viral infection, or tumor development. These processes can be studied by imaging techniques including light and fluorescence microscopy. Quantitative information about biological systems can be inferred from the biological imaging data, however, the mechanisms that cause the spatial patterning often remain elusive. Computational modeling tools are getting more attention as a way to understand the multicellular Spatio-temporal processes. In recent years, there was a focus on imaging, data analysis, and simulation techniques, however, the rigorous parameterization of multicellular models is becoming accessible through the advance in the methods and the computational resources. The parametrization of the multicellular models form high-throughput and high-content (imaging) data is essential to compare competing hypotheses, to understand the multicellular processes, and to predict the perturbation experiments. A method that has been proven to be applicable to multi-cellular models is Approximate Bayesian Computation (ABC). Unfortunately, ABC is a computationally expensive approach, as it requires a large number of simulations. Thus, there is an increased need for a fast and general-purpose pipeline for modeling and simulating multi-cellular systems that can exploit HPC systems for faster computations. To this end, we started the development of a user-friendly, open-source, and scalable platform, called FitMultiCell, which aims to build and validate an open platform for modeling, simulation, and parameter estimation of multicellular systems, which will be utilized to mechanistically answer biomedical questions based on imaging data. To achieve the goal of FitMultiCell, we combine the modeling and simulation tool Morpheus with the advanced statistical inference tool pyABC. In this contribution, we present an overview of the FitMultiCell pipeline and demonstrate an application example. An HCV model that was described by Kumberger et al. (Viruses, 10(4), 2018) was used to illustrate the flow of the platform.