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Microscopy cell nuclei segmentation with enhanced U-Net
BACKGROUND: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usual...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950983/ https://www.ncbi.nlm.nih.gov/pubmed/31914944 http://dx.doi.org/10.1186/s12859-019-3332-1 |
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author | Long, Feixiao |
author_facet | Long, Feixiao |
author_sort | Long, Feixiao |
collection | PubMed |
description | BACKGROUND: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. RESULTS: An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. CONCLUSIONS: Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing. |
format | Online Article Text |
id | pubmed-6950983 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69509832020-01-09 Microscopy cell nuclei segmentation with enhanced U-Net Long, Feixiao BMC Bioinformatics Methodology Article BACKGROUND: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. RESULTS: An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. CONCLUSIONS: Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing. BioMed Central 2020-01-08 /pmc/articles/PMC6950983/ /pubmed/31914944 http://dx.doi.org/10.1186/s12859-019-3332-1 Text en © The Author(s) 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Long, Feixiao Microscopy cell nuclei segmentation with enhanced U-Net |
title | Microscopy cell nuclei segmentation with enhanced U-Net |
title_full | Microscopy cell nuclei segmentation with enhanced U-Net |
title_fullStr | Microscopy cell nuclei segmentation with enhanced U-Net |
title_full_unstemmed | Microscopy cell nuclei segmentation with enhanced U-Net |
title_short | Microscopy cell nuclei segmentation with enhanced U-Net |
title_sort | microscopy cell nuclei segmentation with enhanced u-net |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950983/ https://www.ncbi.nlm.nih.gov/pubmed/31914944 http://dx.doi.org/10.1186/s12859-019-3332-1 |
work_keys_str_mv | AT longfeixiao microscopycellnucleisegmentationwithenhancedunet |