Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866916454821003264 |
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| author | de Dumast, Priscille Kebiri, Hamza Payette, Kelly Jakab, Andras Lajous, Hélène Cuadra, Meritxell Bach |
| author_facet | de Dumast, Priscille Kebiri, Hamza Payette, Kelly Jakab, Andras Lajous, Hélène Cuadra, Meritxell Bach |
| contents | The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brain stem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_04737 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation de Dumast, Priscille Kebiri, Hamza Payette, Kelly Jakab, Andras Lajous, Hélène Cuadra, Meritxell Bach Image and Video Processing Computer Vision and Pattern Recognition The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brain stem. |
| title | Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2111.04737 |