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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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Autor principal: Long, Feixiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
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.
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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
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