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Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants

The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anato...

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Autores principales: Langner, Taro, Östling, Andreas, Maldonis, Lukas, Karlsson, Albin, Olmo, Daniel, Lindgren, Dag, Wallin, Andreas, Lundin, Lowe, Strand, Robin, Ahlström, Håkan, Kullberg, Joel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708493/
https://www.ncbi.nlm.nih.gov/pubmed/33262432
http://dx.doi.org/10.1038/s41598-020-77981-4
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author Langner, Taro
Östling, Andreas
Maldonis, Lukas
Karlsson, Albin
Olmo, Daniel
Lindgren, Dag
Wallin, Andreas
Lundin, Lowe
Strand, Robin
Ahlström, Håkan
Kullberg, Joel
author_facet Langner, Taro
Östling, Andreas
Maldonis, Lukas
Karlsson, Albin
Olmo, Daniel
Lindgren, Dag
Wallin, Andreas
Lundin, Lowe
Strand, Robin
Ahlström, Håkan
Kullberg, Joel
author_sort Langner, Taro
collection PubMed
description The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.
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spelling pubmed-77084932020-12-03 Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants Langner, Taro Östling, Andreas Maldonis, Lukas Karlsson, Albin Olmo, Daniel Lindgren, Dag Wallin, Andreas Lundin, Lowe Strand, Robin Ahlström, Håkan Kullberg, Joel Sci Rep Article The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume. Nature Publishing Group UK 2020-12-01 /pmc/articles/PMC7708493/ /pubmed/33262432 http://dx.doi.org/10.1038/s41598-020-77981-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Langner, Taro
Östling, Andreas
Maldonis, Lukas
Karlsson, Albin
Olmo, Daniel
Lindgren, Dag
Wallin, Andreas
Lundin, Lowe
Strand, Robin
Ahlström, Håkan
Kullberg, Joel
Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title_full Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title_fullStr Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title_full_unstemmed Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title_short Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
title_sort kidney segmentation in neck-to-knee body mri of 40,000 uk biobank participants
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708493/
https://www.ncbi.nlm.nih.gov/pubmed/33262432
http://dx.doi.org/10.1038/s41598-020-77981-4
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