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AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study
Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840251/ https://www.ncbi.nlm.nih.gov/pubmed/36641470 http://dx.doi.org/10.1186/s40478-023-01509-w |
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author | Wang, Shuncong Pang, Xin de Keyzer, Frederik Feng, Yuanbo Swinnen, Johan V. Yu, Jie Ni, Yicheng |
author_facet | Wang, Shuncong Pang, Xin de Keyzer, Frederik Feng, Yuanbo Swinnen, Johan V. Yu, Jie Ni, Yicheng |
author_sort | Wang, Shuncong |
collection | PubMed |
description | Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal–noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40478-023-01509-w. |
format | Online Article Text |
id | pubmed-9840251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98402512023-01-15 AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study Wang, Shuncong Pang, Xin de Keyzer, Frederik Feng, Yuanbo Swinnen, Johan V. Yu, Jie Ni, Yicheng Acta Neuropathol Commun Research Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal–noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40478-023-01509-w. BioMed Central 2023-01-14 /pmc/articles/PMC9840251/ /pubmed/36641470 http://dx.doi.org/10.1186/s40478-023-01509-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wang, Shuncong Pang, Xin de Keyzer, Frederik Feng, Yuanbo Swinnen, Johan V. Yu, Jie Ni, Yicheng AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title | AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title_full | AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title_fullStr | AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title_full_unstemmed | AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title_short | AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
title_sort | ai-based mri auto-segmentation of brain tumor in rodents, a multicenter study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840251/ https://www.ncbi.nlm.nih.gov/pubmed/36641470 http://dx.doi.org/10.1186/s40478-023-01509-w |
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