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Tracking cell lineages in 3D by incremental deep learning
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELE...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741210/ https://www.ncbi.nlm.nih.gov/pubmed/34989675 http://dx.doi.org/10.7554/eLife.69380 |
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author | Sugawara, Ko Çevrim, Çağrı Averof, Michalis |
author_facet | Sugawara, Ko Çevrim, Çağrı Averof, Michalis |
author_sort | Sugawara, Ko |
collection | PubMed |
description | Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software’s performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort. |
format | Online Article Text |
id | pubmed-8741210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-87412102022-01-11 Tracking cell lineages in 3D by incremental deep learning Sugawara, Ko Çevrim, Çağrı Averof, Michalis eLife Developmental Biology Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software’s performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort. eLife Sciences Publications, Ltd 2022-01-06 /pmc/articles/PMC8741210/ /pubmed/34989675 http://dx.doi.org/10.7554/eLife.69380 Text en © 2022, Sugawara et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Developmental Biology Sugawara, Ko Çevrim, Çağrı Averof, Michalis Tracking cell lineages in 3D by incremental deep learning |
title | Tracking cell lineages in 3D by incremental deep learning |
title_full | Tracking cell lineages in 3D by incremental deep learning |
title_fullStr | Tracking cell lineages in 3D by incremental deep learning |
title_full_unstemmed | Tracking cell lineages in 3D by incremental deep learning |
title_short | Tracking cell lineages in 3D by incremental deep learning |
title_sort | tracking cell lineages in 3d by incremental deep learning |
topic | Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741210/ https://www.ncbi.nlm.nih.gov/pubmed/34989675 http://dx.doi.org/10.7554/eLife.69380 |
work_keys_str_mv | AT sugawarako trackingcelllineagesin3dbyincrementaldeeplearning AT cevrimcagrı trackingcelllineagesin3dbyincrementaldeeplearning AT averofmichalis trackingcelllineagesin3dbyincrementaldeeplearning |