Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866909365715337216 |
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| author | de Dumast, Priscille Cuadra, Meritxell Bach |
| author_facet | de Dumast, Priscille Cuadra, Meritxell Bach |
| contents | Quantitative analysis of in utero human brain development is crucial for abnormal characterization. Magnetic resonance image (MRI) segmentation is therefore an asset for quantitative analysis. However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus as an efficient tuning-free data augmentation strategy. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_14282 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation de Dumast, Priscille Cuadra, Meritxell Bach Image and Video Processing Computer Vision and Pattern Recognition Quantitative analysis of in utero human brain development is crucial for abnormal characterization. Magnetic resonance image (MRI) segmentation is therefore an asset for quantitative analysis. However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus as an efficient tuning-free data augmentation strategy. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines. |
| title | Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.14282 |