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...
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| Published in: | Frontiers in neuroscience Vol. 16; p. 975862 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Lausanne
Frontiers Research Foundation
26.10.2022
Frontiers Frontiers Media S.A |
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| ISSN: | 1662-453X, 1662-4548, 1662-453X |
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| Abstract | 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
. |
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| AbstractList | 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 . 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 F1 score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet. 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 . 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 F1 score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet.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 F1 score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet. |
| Author | Ashtari, Pooya Sappey-Marinier, Dominique Barile, Berardino Van Huffel, Sabine |
| AuthorAffiliation | 2 CREATIS (UMR 5220 CNRS – U1294 INSERM), Université Claude Bernard Lyon 1, Université de Lyon , Villeurbanne , France 1 Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven , Leuven , Belgium |
| AuthorAffiliation_xml | – name: 2 CREATIS (UMR 5220 CNRS – U1294 INSERM), Université Claude Bernard Lyon 1, Université de Lyon , Villeurbanne , France – name: 1 Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven , Leuven , Belgium |
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| Cites_doi | 10.1007/978-3-030-01261-8_1 10.1136/jnnp.55.8.671 10.1109/CVPR.2017.243 10.1109/3DV.2016.79 10.1177/1352458518815602 10.1016/j.neuroimage.2019.03.068 10.1038/s41598-018-31911-7 10.1007/978-3-319-46723-8_49 10.5281/zenodo.6114127 10.1016/j.compmedimag.2020.101772 10.1038/s41592-020-01008-z 10.1007/978-3-319-24574-4_28 10.1109/CVPR.2016.90 10.1109/ICCV.2017.324 10.1016/j.nicl.2020.102335 |
| ContentType | Journal Article |
| Copyright | 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Ashtari, Barile, Van Huffel and Sappey-Marinier. Distributed under a Creative Commons Attribution 4.0 International License Copyright © 2022 Ashtari, Barile, Van Huffel and Sappey-Marinier. 2022 Ashtari, Barile, Van Huffel and Sappey-Marinier |
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| References | B21 La Rosa (B13) 2020; 27 Isensee (B12) 2019 B14 McKinley (B16) 2018 Myronenko (B19) 2019 Ronneberger (B22) 2015 Wu (B24) 2018 Commowick (B5) 2018; 8 Çiçek (B4) 2016 B1 Huang (B10) 2017 Vukusic (B23) 2020; 26 (B18) 2020 Nills (B20) 2020; 84 B7 Isensee (B11) 2020; 18 Lin (B15) 2017 Ashtari (B2) Aslani (B3) 2019; 196 He (B9) He (B8) Confavreux (B6) 1992; 55 Milletari (B17) 2016 |
| References_xml | – start-page: 3 year: 2018 ident: B24 article-title: Group normalization publication-title: Proceedings of the European Conference on Computer Vision (ECCV) doi: 10.1007/978-3-030-01261-8_1 – volume: 55 start-page: 671 year: 1992 ident: B6 article-title: EDMUS, a European database for multiple sclerosis publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp.55.8.671 – ident: B14 – start-page: 4700 year: 2017 ident: B10 article-title: “Densely connected convolutional networks,” publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE) doi: 10.1109/CVPR.2017.243 – start-page: 565 year: 2016 ident: B17 article-title: “V-Net: fully convolutional neural networks for volumetric medical image segmentation,” publication-title: 2016 Fourth International Conference on 3D Vision doi: 10.1109/3DV.2016.79 – volume: 26 start-page: 118 year: 2020 ident: B23 article-title: Observatoire français de la sclérose en plaques (OFSEP): a unique multimodal nationwide MS registry in France publication-title: Multip. Scler. doi: 10.1177/1352458518815602 – volume: 196 start-page: 1 year: 2019 ident: B3 article-title: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation publication-title: NeuroImage doi: 10.1016/j.neuroimage.2019.03.068 – volume: 8 start-page: 1 year: 2018 ident: B5 article-title: Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure publication-title: Sci. Rep doi: 10.1038/s41598-018-31911-7 – start-page: 424 volume-title: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016 year: 2016 ident: B4 article-title: 3D U-Net: learning dense volumetric segmentation from sparse annotation doi: 10.1007/978-3-319-46723-8_49 – start-page: 630 ident: B9 article-title: “Identity mappings in deep residual networks,” in Computer Vision – ECCV 2016 – year: 2020 ident: B18 article-title: MONAI: Medical Open Network for AI publication-title: Zenodo doi: 10.5281/zenodo.6114127 – ident: B1 – start-page: 470 volume-title: International MICCAI Brainlesion Workshop: BrainLes 2020. Lecture Notes in Computer Science, Vol. 12658 ident: B2 article-title: Low-rank convolutional networks for brain tumor segmentation – ident: B7 – start-page: 234 volume-title: International MICCAI Brainlesion Workshop: BrainLes 2018. Lecture Notes in Computer Science year: 2019 ident: B12 article-title: No New-Net – volume: 84 start-page: 101772 year: 2020 ident: B20 article-title: Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs publication-title: Computer. Med. Imag. Graph doi: 10.1016/j.compmedimag.2020.101772 – volume: 18 start-page: 203 year: 2020 ident: B11 article-title: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation publication-title: Nat. Methods doi: 10.1038/s41592-020-01008-z – ident: B21 – start-page: 234 volume-title: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015 year: 2015 ident: B22 article-title: U-Net: convolutional networks for biomedical image segmentation doi: 10.1007/978-3-319-24574-4_28 – start-page: 770 ident: B8 article-title: “Deep residual learning for image recognition,” publication-title: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition (IEEE) doi: 10.1109/CVPR.2016.90 – start-page: 2980 year: 2017 ident: B15 article-title: “Focal loss for dense object detection,” publication-title: Proceedings of the IEEE International Conference on Computer Vision (IEEE) doi: 10.1109/ICCV.2017.324 – start-page: 456 volume-title: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries year: 2018 ident: B16 article-title: Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation – start-page: 311 volume-title: International MICCAI Brainlesion Workshop: BrainLes 2018. Lecture Notes in Computer Science, Vol. 11384 year: 2019 ident: B19 article-title: 3D MRI brain tumor segmentation using autoencoder regularization – volume: 27 start-page: 102335 year: 2020 ident: B13 article-title: Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE publication-title: NeuroImage. Clin doi: 10.1016/j.nicl.2020.102335 |
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| SubjectTerms | Brain cancer Computer Science Experiments Lesions Medical Imaging Multiple sclerosis Neural networks Neuroscience new lesions pre-activation Segmentation Semantics U-Net |
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| Title | New multiple sclerosis lesion segmentation and detection using pre-activation U-Net |
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