DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917955194847232 |
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| author | Ruiz-Perez, Marina Morell-Ortega, Sergio Gadea, Marien Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Mariam Tourdias, Thomas Coupé, Pierrick Manjón, José V. |
| author_facet | Ruiz-Perez, Marina Morell-Ortega, Sergio Gadea, Marien Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Mariam Tourdias, Thomas Coupé, Pierrick Manjón, José V. |
| contents | The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented a multimodal volumetric deep neural network for the segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Current tools either operate at standard resolution (1 mm3) or use monomodal data. To achieve the proposed objective, first, a database of semiautomatically segmented thalamic nuclei was created using ultra-high resolution T1, T2 and White Matter nulled (WMn) images. Then, a novel Deep learning based strategy was designed to obtain the automatic segmentations and trained to improve its robustness and accuaracy using a semisupervised approach. The proposed method was compared with a related state-of-the-art method showing competitive results both in terms of segmentation quality and efficiency. To make the proposed method fully available to the scientific community, a full pipeline able to work with monomodal standard resolution T1 images is also proposed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07751 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI Ruiz-Perez, Marina Morell-Ortega, Sergio Gadea, Marien Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Mariam Tourdias, Thomas Coupé, Pierrick Manjón, José V. Image and Video Processing Computer Vision and Pattern Recognition The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented a multimodal volumetric deep neural network for the segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Current tools either operate at standard resolution (1 mm3) or use monomodal data. To achieve the proposed objective, first, a database of semiautomatically segmented thalamic nuclei was created using ultra-high resolution T1, T2 and White Matter nulled (WMn) images. Then, a novel Deep learning based strategy was designed to obtain the automatic segmentations and trained to improve its robustness and accuaracy using a semisupervised approach. The proposed method was compared with a related state-of-the-art method showing competitive results both in terms of segmentation quality and efficiency. To make the proposed method fully available to the scientific community, a full pipeline able to work with monomodal standard resolution T1 images is also proposed. |
| title | DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07751 |