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Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excel...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553833/ https://www.ncbi.nlm.nih.gov/pubmed/34711849 http://dx.doi.org/10.1038/s41597-021-01064-w |
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author | Shapey, Jonathan Kujawa, Aaron Dorent, Reuben Wang, Guotai Dimitriadis, Alexis Grishchuk, Diana Paddick, Ian Kitchen, Neil Bradford, Robert Saeed, Shakeel R. Bisdas, Sotirios Ourselin, Sébastien Vercauteren, Tom |
author_facet | Shapey, Jonathan Kujawa, Aaron Dorent, Reuben Wang, Guotai Dimitriadis, Alexis Grishchuk, Diana Paddick, Ian Kitchen, Neil Bradford, Robert Saeed, Shakeel R. Bisdas, Sotirios Ourselin, Sébastien Vercauteren, Tom |
author_sort | Shapey, Jonathan |
collection | PubMed |
description | Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models. |
format | Online Article Text |
id | pubmed-8553833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85538332021-10-29 Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm Shapey, Jonathan Kujawa, Aaron Dorent, Reuben Wang, Guotai Dimitriadis, Alexis Grishchuk, Diana Paddick, Ian Kitchen, Neil Bradford, Robert Saeed, Shakeel R. Bisdas, Sotirios Ourselin, Sébastien Vercauteren, Tom Sci Data Data Descriptor Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models. Nature Publishing Group UK 2021-10-28 /pmc/articles/PMC8553833/ /pubmed/34711849 http://dx.doi.org/10.1038/s41597-021-01064-w Text en © The Author(s) 2021 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, 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 metadata files associated with this article. |
spellingShingle | Data Descriptor Shapey, Jonathan Kujawa, Aaron Dorent, Reuben Wang, Guotai Dimitriadis, Alexis Grishchuk, Diana Paddick, Ian Kitchen, Neil Bradford, Robert Saeed, Shakeel R. Bisdas, Sotirios Ourselin, Sébastien Vercauteren, Tom Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title | Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title_full | Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title_fullStr | Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title_full_unstemmed | Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title_short | Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm |
title_sort | segmentation of vestibular schwannoma from mri, an open annotated dataset and baseline algorithm |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553833/ https://www.ncbi.nlm.nih.gov/pubmed/34711849 http://dx.doi.org/10.1038/s41597-021-01064-w |
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