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New multiple sclerosis lesion segmentation and detection using pre-activation U-Net
Automated segmentation of new multiple sclerosis (MS) lesions in 3D MRI data is an essential prerequisite for monitoring and quantifying MS progression. Manual delineation of such lesions is time-consuming and expensive, especially because raters need to deal with 3D images and several modalities. I...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646406/ https://www.ncbi.nlm.nih.gov/pubmed/36389254 http://dx.doi.org/10.3389/fnins.2022.975862 |
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author | Ashtari, Pooya Barile, Berardino Van Huffel, Sabine Sappey-Marinier, Dominique |
author_facet | Ashtari, Pooya Barile, Berardino Van Huffel, Sabine Sappey-Marinier, Dominique |
author_sort | Ashtari, Pooya |
collection | PubMed |
description | Automated segmentation of new multiple sclerosis (MS) lesions in 3D MRI data is an essential prerequisite for monitoring and quantifying MS progression. Manual delineation of such lesions is time-consuming and expensive, especially because raters need to deal with 3D images and several modalities. In this paper, we propose Pre-U-Net, a 3D encoder-decoder architecture with pre-activation residual blocks, for the segmentation and detection of new MS lesions. Due to the limited training set and the class imbalance problem, we apply intensive data augmentation and use deep supervision to train our models effectively. Following the same U-shaped architecture but different blocks, Pre-U-Net outperforms U-Net and Res-U-Net on the MSSEG-2 dataset, achieving a Dice score of 40.3% on new lesion segmentation and an F(1) score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet. |
format | Online Article Text |
id | pubmed-9646406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96464062022-11-15 New multiple sclerosis lesion segmentation and detection using pre-activation U-Net Ashtari, Pooya Barile, Berardino Van Huffel, Sabine Sappey-Marinier, Dominique Front Neurosci Neuroscience Automated segmentation of new multiple sclerosis (MS) lesions in 3D MRI data is an essential prerequisite for monitoring and quantifying MS progression. Manual delineation of such lesions is time-consuming and expensive, especially because raters need to deal with 3D images and several modalities. In this paper, we propose Pre-U-Net, a 3D encoder-decoder architecture with pre-activation residual blocks, for the segmentation and detection of new MS lesions. Due to the limited training set and the class imbalance problem, we apply intensive data augmentation and use deep supervision to train our models effectively. Following the same U-shaped architecture but different blocks, Pre-U-Net outperforms U-Net and Res-U-Net on the MSSEG-2 dataset, achieving a Dice score of 40.3% on new lesion segmentation and an F(1) score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9646406/ /pubmed/36389254 http://dx.doi.org/10.3389/fnins.2022.975862 Text en Copyright © 2022 Ashtari, Barile, Van Huffel and Sappey-Marinier. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ashtari, Pooya Barile, Berardino Van Huffel, Sabine Sappey-Marinier, Dominique New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title_full | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title_fullStr | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title_full_unstemmed | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title_short | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
title_sort | new multiple sclerosis lesion segmentation and detection using pre-activation u-net |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646406/ https://www.ncbi.nlm.nih.gov/pubmed/36389254 http://dx.doi.org/10.3389/fnins.2022.975862 |
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