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A convolutional neural network for segmentation of yeast cells without manual training annotations
MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challeng...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825468/ https://www.ncbi.nlm.nih.gov/pubmed/34893817 http://dx.doi.org/10.1093/bioinformatics/btab835 |
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author | Kruitbosch, Herbert T Mzayek, Yasmin Omlor, Sara Guerra, Paolo Milias-Argeitis, Andreas |
author_facet | Kruitbosch, Herbert T Mzayek, Yasmin Omlor, Sara Guerra, Paolo Milias-Argeitis, Andreas |
author_sort | Kruitbosch, Herbert T |
collection | PubMed |
description | MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8825468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-88254682022-02-09 A convolutional neural network for segmentation of yeast cells without manual training annotations Kruitbosch, Herbert T Mzayek, Yasmin Omlor, Sara Guerra, Paolo Milias-Argeitis, Andreas Bioinformatics Original Papers MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-12-10 /pmc/articles/PMC8825468/ /pubmed/34893817 http://dx.doi.org/10.1093/bioinformatics/btab835 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Kruitbosch, Herbert T Mzayek, Yasmin Omlor, Sara Guerra, Paolo Milias-Argeitis, Andreas A convolutional neural network for segmentation of yeast cells without manual training annotations |
title | A convolutional neural network for segmentation of yeast cells without manual training annotations |
title_full | A convolutional neural network for segmentation of yeast cells without manual training annotations |
title_fullStr | A convolutional neural network for segmentation of yeast cells without manual training annotations |
title_full_unstemmed | A convolutional neural network for segmentation of yeast cells without manual training annotations |
title_short | A convolutional neural network for segmentation of yeast cells without manual training annotations |
title_sort | convolutional neural network for segmentation of yeast cells without manual training annotations |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825468/ https://www.ncbi.nlm.nih.gov/pubmed/34893817 http://dx.doi.org/10.1093/bioinformatics/btab835 |
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