Work

DeLTA 3.0: A modern segmentation and tracking tool for microscopy images

Year: 2026

Type: article

Abstract: Time-lapse microscopy is a popular approach for quantifying single-cell dynamics.Analyzing image sequences requires two steps: segmentation (locating the cells) and tracking (following them).DeLTA (Deep Learning for Time-lapse Analysis) is a Python package that performs segmentation and tracking.Initially developed to handle images of bacteria growing within the "mother machine," (Wang et al., 2010) a popular microfluidic device for single-cell analysis that constrains cells to grow in one dimension (Lugagne et al., 2020), it was later extended to handle cells growing in 2D (O'Connor et al., 2022).After three years of experience and feedback, we are releasing version 3.0 -a major overhaul to achieve four objectives: enhance usability for scientists, improve modularity and adaptability for developers, integrate features like growth rate computation and lineage manipulation, and adopt modern open-source development practices.

Source: The Journal of Open Source Software

DOI: 10.21105/joss.10279

Authors: Virgile Andreani, Jean-Baptiste Lugagne, Owen M. O’Connor, Mary J. Dunlop

Institutions: Boston University, Centre Inria de Saclay, University of Oxford

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

Funder: National Science Foundation

Award: Transitions: Deep Learning Models for Microbial Image Analysis and Time-Series Predictions