Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

Front Phys. 2020 Jan:7:247. doi: 10.3389/fphy.2019.00247. Epub 2020 Jan 21.

Abstract

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.

Keywords: cellular structures; machine learning; meshing; reconstruction; segmentation; simulation.