Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| author | Manjón, José V. Morell-Ortega, Sergio Ruiz-Perez, Marina Mansencal, Boris Bot, Edern Le Gadea, Marien Lanuza, Enrique Catheline, Gwenaelle Tourdias, Thomas Planche, Vincent Giraud, Rémi Rivière, Denis Mangin, Jean-François Labra-Avila, Nicole Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Maria Coupé, Pierrick |
| author_facet | Manjón, José V. Morell-Ortega, Sergio Ruiz-Perez, Marina Mansencal, Boris Bot, Edern Le Gadea, Marien Lanuza, Enrique Catheline, Gwenaelle Tourdias, Thomas Planche, Vincent Giraud, Rémi Rivière, Denis Mangin, Jean-François Labra-Avila, Nicole Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Maria Coupé, Pierrick |
| contents | In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of the human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical levels from the organ to the substructure level, using a new densely labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas was constructed by averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution were selected for this project. The images of these 75 subjects were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the proposed atlas has 350 different labels derived from 7 distinct delineation protocols. These labels were grouped at multiple scales, offering a coherent and consistent holistic representation of the brain across different levels of detail. This multiscale and multimodal atlas can be used to develop new ultra-high-resolution segmentation methods, potentially improving the early detection of neurological disorders. We make it publicly available to the scientific community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16879 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas Manjón, José V. Morell-Ortega, Sergio Ruiz-Perez, Marina Mansencal, Boris Bot, Edern Le Gadea, Marien Lanuza, Enrique Catheline, Gwenaelle Tourdias, Thomas Planche, Vincent Giraud, Rémi Rivière, Denis Mangin, Jean-François Labra-Avila, Nicole Vivo-Hernando, Roberto Rubio, Gregorio Aparici, Fernando de la Iglesia-Vaya, Maria Coupé, Pierrick Image and Video Processing Computer Vision and Pattern Recognition In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of the human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical levels from the organ to the substructure level, using a new densely labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas was constructed by averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution were selected for this project. The images of these 75 subjects were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the proposed atlas has 350 different labels derived from 7 distinct delineation protocols. These labels were grouped at multiple scales, offering a coherent and consistent holistic representation of the brain across different levels of detail. This multiscale and multimodal atlas can be used to develop new ultra-high-resolution segmentation methods, potentially improving the early detection of neurological disorders. We make it publicly available to the scientific community. |
| title | Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.16879 |