Soelistyo, Christopher;
Ulicna, Kristina;
Lowe, Alan;
(2023)
Machine learning enhanced cell tracking.
Frontiers in Bioinformatics
, 3
, Article 1228989. 10.3389/fbinf.2023.1228989.
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Abstract
Quantifying cell biology in space and time requires computational methods to detect cells, measure their properties, and assemble these into meaningful trajectories. In this aspect, machine learning (ML) is having a transformational effect on bioimage analysis, now enabling robust cell detection in multidimensional image data. However, the task of cell tracking, or constructing accurate multi-generational lineages from imaging data, remains an open challenge. Most cell tracking algorithms are largely based on our prior knowledge of cell behaviors, and as such, are difficult to generalize to new and unseen cell types or datasets. Here, we propose that ML provides the framework to learn aspects of cell behavior using cell tracking as the task to be learned. We suggest that advances in representation learning, cell tracking datasets, metrics, and methods for constructing and evaluating tracking solutions can all form part of an end-to-end ML-enhanced pipeline. These developments will lead the way to new computational methods that can be used to understand complex, time-evolving biological systems.
| Type: | Article |
|---|---|
| Title: | Machine learning enhanced cell tracking |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fbinf.2023.1228989 |
| Publisher version: | https://doi.org/10.3389/fbinf.2023.1228989 |
| Language: | English |
| Additional information: | © 2023 Soelistyo, Ulicna and Lowe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Machine learning (ML), computer vision, tracking, cell tracking, bioimage analysis, optimisation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172796 |
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