As molecular dynamics
simulations increase in complexity, new analysis
tools are necessary to facilitate interpreting the results. Lipids,
for instance, are known to form many complicated morphologies, because
of their amphipathic nature, becoming more intricate as the particle
count increases. A few lipids might form a micelle, where aggregation
of tens of thousands could lead to vesicle formation. Millions of
lipids comprise a cell and its organelle membranes, and are involved
in processes such as neurotransmission and transfection. To study
such phenomena, it is useful to have analysis tools that understand
what is meant by emerging entities such as micelles and vesicles.
Studying such systems at the particle level only becomes extremely
tedious, counterintuitive, and computationally expensive. To address
this issue, we developed a method to track all the individual lipid
leaflets, allowing for easy and quick detection of topological changes
at the mesoscale. By using a voxel-based approach and focusing on
locality, we forego costly geometrical operations without losing important
details and chronologically identify the lipid segments using the
Jaccard index. Thus, we achieve a consistent sequential segmentation
on a wide variety of (lipid) systems, including monolayers, bilayers,
vesicles, inverted hexagonal phases, up to the membranes of a full
mitochondrion. It also discriminates between adhesion and fusion of
leaflets. We show that our method produces consistent results without
the need for prefitting parameters, and segmentation of millions of
particles can be achieved on a desktop machine.