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LapTrack: linear assignment particle tracking with tunable metrics
MOTIVATION: Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825786/ https://www.ncbi.nlm.nih.gov/pubmed/36495181 http://dx.doi.org/10.1093/bioinformatics/btac799 |
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author | Fukai, Yohsuke T Kawaguchi, Kyogo |
author_facet | Fukai, Yohsuke T Kawaguchi, Kyogo |
author_sort | Fukai, Yohsuke T |
collection | PubMed |
description | MOTIVATION: Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. RESULTS: We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation and visualization. AVAILABILITY AND IMPLEMENTATION: LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9825786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98257862023-01-10 LapTrack: linear assignment particle tracking with tunable metrics Fukai, Yohsuke T Kawaguchi, Kyogo Bioinformatics Original Paper MOTIVATION: Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. RESULTS: We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation and visualization. AVAILABILITY AND IMPLEMENTATION: LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-10 /pmc/articles/PMC9825786/ /pubmed/36495181 http://dx.doi.org/10.1093/bioinformatics/btac799 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Fukai, Yohsuke T Kawaguchi, Kyogo LapTrack: linear assignment particle tracking with tunable metrics |
title | LapTrack: linear assignment particle tracking with tunable metrics |
title_full | LapTrack: linear assignment particle tracking with tunable metrics |
title_fullStr | LapTrack: linear assignment particle tracking with tunable metrics |
title_full_unstemmed | LapTrack: linear assignment particle tracking with tunable metrics |
title_short | LapTrack: linear assignment particle tracking with tunable metrics |
title_sort | laptrack: linear assignment particle tracking with tunable metrics |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825786/ https://www.ncbi.nlm.nih.gov/pubmed/36495181 http://dx.doi.org/10.1093/bioinformatics/btac799 |
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