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U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic reson...

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Autores principales: Praschl, Christoph, Zopf, Lydia M., Kiemeyer, Emma, Langthallner, Ines, Ritzberger, Daniel, Slowak, Adrian, Weigl, Martin, Blüml, Valentin, Nešić, Nebojša, Stojmenović, Miloš, Kniewallner, Kathrin M., Aigner, Ludwig, Winkler, Stephan, Walter, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569551/
https://www.ncbi.nlm.nih.gov/pubmed/37824474
http://dx.doi.org/10.1371/journal.pone.0291946
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author Praschl, Christoph
Zopf, Lydia M.
Kiemeyer, Emma
Langthallner, Ines
Ritzberger, Daniel
Slowak, Adrian
Weigl, Martin
Blüml, Valentin
Nešić, Nebojša
Stojmenović, Miloš
Kniewallner, Kathrin M.
Aigner, Ludwig
Winkler, Stephan
Walter, Andreas
author_facet Praschl, Christoph
Zopf, Lydia M.
Kiemeyer, Emma
Langthallner, Ines
Ritzberger, Daniel
Slowak, Adrian
Weigl, Martin
Blüml, Valentin
Nešić, Nebojša
Stojmenović, Miloš
Kniewallner, Kathrin M.
Aigner, Ludwig
Winkler, Stephan
Walter, Andreas
author_sort Praschl, Christoph
collection PubMed
description Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
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spelling pubmed-105695512023-10-13 U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets Praschl, Christoph Zopf, Lydia M. Kiemeyer, Emma Langthallner, Ines Ritzberger, Daniel Slowak, Adrian Weigl, Martin Blüml, Valentin Nešić, Nebojša Stojmenović, Miloš Kniewallner, Kathrin M. Aigner, Ludwig Winkler, Stephan Walter, Andreas PLoS One Research Article Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset. Public Library of Science 2023-10-12 /pmc/articles/PMC10569551/ /pubmed/37824474 http://dx.doi.org/10.1371/journal.pone.0291946 Text en © 2023 Praschl et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Praschl, Christoph
Zopf, Lydia M.
Kiemeyer, Emma
Langthallner, Ines
Ritzberger, Daniel
Slowak, Adrian
Weigl, Martin
Blüml, Valentin
Nešić, Nebojša
Stojmenović, Miloš
Kniewallner, Kathrin M.
Aigner, Ludwig
Winkler, Stephan
Walter, Andreas
U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title_full U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title_fullStr U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title_full_unstemmed U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title_short U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
title_sort u-net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569551/
https://www.ncbi.nlm.nih.gov/pubmed/37824474
http://dx.doi.org/10.1371/journal.pone.0291946
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