A light-weight, data-driven segmentation method for multi-state Brownian trajectories

Journal article

Single-particle tracking methods have emerged as a crucial tool for the characterisation of dynamical and diffusive processes in a range of biological and synthetic systems. Here, we propose a simple and light-weight yet accurate method for the segmentation of multi-state Brownian trajectories based on an optimised Gaussian filtering of the displacement time series combined with an automated fitting to a Gaussian mixture model. We verify our method using synthetic, 2-state Brownian trajectories and show that our method provides high levels of accuracy in terms of segmentation and the estimation of self-diffusion coefficients for reasonably well-separated values of the diffusion coefficients. We furthermore demonstrate the feasibility of our method on experimental systems using single-particle tracking data for diffusing membrane proteins bound to a supported lipid bilayer. Compared to methods based on deep learning or hidden Markov models, our method imposes a much lower computational load, making it suitable for fast and accurate online processing of single-particle trajectories from microscopy images.

Authors: El Korde, I; Lewis, JM; Clarkson, E; Dam, T; Jönsson, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: npj Biological Physics and Mechanics
• Volume: 3
• Issue: 1
• Article number: 6
• Publication date: 2026-05-08
• Acceptance date: 2026-03-07
• DOI: 10.1038/s44341-026-00037-7
• EISSN: 3004-863X
• ISSN: 3004-863X
• Language: English
• Keywords: Tracking (education); Segmentation; Gaussian process; Gaussian; Range (aeronautics); Brownian motion; Markov process; Displacement (psychology); Image segmentation; Diffusion