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Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency
Segmentation of medical images into different tissue types is essential for many advancements in orthopaedic research; however, manual segmentation techniques can be time- and cost-prohibitive. The purpose of this work was to develop a semi-automatic segmentation algorithm that leverages gradients i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098419/ https://www.ncbi.nlm.nih.gov/pubmed/35551485 http://dx.doi.org/10.1038/s41598-022-11785-6 |
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author | Heckelman, Lauren N. Soher, Brian J. Spritzer, Charles E. Lewis, Brian D. DeFrate, Louis E. |
author_facet | Heckelman, Lauren N. Soher, Brian J. Spritzer, Charles E. Lewis, Brian D. DeFrate, Louis E. |
author_sort | Heckelman, Lauren N. |
collection | PubMed |
description | Segmentation of medical images into different tissue types is essential for many advancements in orthopaedic research; however, manual segmentation techniques can be time- and cost-prohibitive. The purpose of this work was to develop a semi-automatic segmentation algorithm that leverages gradients in spatial intensity to isolate the patella bone from magnetic resonance (MR) images of the knee that does not require a training set. The developed algorithm was validated in a sample of four human participants (in vivo) and three porcine stifle joints (ex vivo) using both magnetic resonance imaging (MRI) and computed tomography (CT). We assessed the repeatability (expressed as mean ± standard deviation) of the semi-automatic segmentation technique on: (1) the same MRI scan twice (Dice similarity coefficient = 0.988 ± 0.002; surface distance = − 0.01 ± 0.001 mm), (2) the scan/re-scan repeatability of the segmentation technique (surface distance = − 0.02 ± 0.03 mm), (3) how the semi-automatic segmentation technique compared to manual MRI segmentation (surface distance = − 0.02 ± 0.08 mm), and (4) how the semi-automatic segmentation technique compared when applied to both MRI and CT images of the same specimens (surface distance = − 0.02 ± 0.06 mm). Mean surface distances perpendicular to the cartilage surface were computed between pairs of patellar bone models. Critically, the semi-automatic segmentation algorithm developed in this work reduced segmentation time by approximately 75%. This method is promising for improving research throughput and potentially for use in generating training data for deep learning algorithms. |
format | Online Article Text |
id | pubmed-9098419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90984192022-05-14 Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency Heckelman, Lauren N. Soher, Brian J. Spritzer, Charles E. Lewis, Brian D. DeFrate, Louis E. Sci Rep Article Segmentation of medical images into different tissue types is essential for many advancements in orthopaedic research; however, manual segmentation techniques can be time- and cost-prohibitive. The purpose of this work was to develop a semi-automatic segmentation algorithm that leverages gradients in spatial intensity to isolate the patella bone from magnetic resonance (MR) images of the knee that does not require a training set. The developed algorithm was validated in a sample of four human participants (in vivo) and three porcine stifle joints (ex vivo) using both magnetic resonance imaging (MRI) and computed tomography (CT). We assessed the repeatability (expressed as mean ± standard deviation) of the semi-automatic segmentation technique on: (1) the same MRI scan twice (Dice similarity coefficient = 0.988 ± 0.002; surface distance = − 0.01 ± 0.001 mm), (2) the scan/re-scan repeatability of the segmentation technique (surface distance = − 0.02 ± 0.03 mm), (3) how the semi-automatic segmentation technique compared to manual MRI segmentation (surface distance = − 0.02 ± 0.08 mm), and (4) how the semi-automatic segmentation technique compared when applied to both MRI and CT images of the same specimens (surface distance = − 0.02 ± 0.06 mm). Mean surface distances perpendicular to the cartilage surface were computed between pairs of patellar bone models. Critically, the semi-automatic segmentation algorithm developed in this work reduced segmentation time by approximately 75%. This method is promising for improving research throughput and potentially for use in generating training data for deep learning algorithms. Nature Publishing Group UK 2022-05-12 /pmc/articles/PMC9098419/ /pubmed/35551485 http://dx.doi.org/10.1038/s41598-022-11785-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Heckelman, Lauren N. Soher, Brian J. Spritzer, Charles E. Lewis, Brian D. DeFrate, Louis E. Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title | Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title_full | Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title_fullStr | Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title_full_unstemmed | Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title_short | Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency |
title_sort | design and validation of a semi-automatic bone segmentation algorithm from mri to improve research efficiency |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098419/ https://www.ncbi.nlm.nih.gov/pubmed/35551485 http://dx.doi.org/10.1038/s41598-022-11785-6 |
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