Automatic skull segmentation of MRI volume, a deep learning model trained on synthetic data
The neuro-imaging community has focus on brain, and therefore the first preprocessing step is often to perform a brain mask, throwing out everything outside the brain. There is then a lack of tool for segmenting the skull, which is a tissue of interest for Paleoanthropology. We propose in this work...
Zapisane w:
| Wydane w: | URI:https://journals.openedition.org/bmsap, |
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| Główni autorzy: | , , , , , |
| Format: | Article ou chapitre numérique |
| Język: | gb |
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Bulletins et mémoires de la société d’anthropologie de Paris
2025
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| Dostęp online: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Streszczenie: | The neuro-imaging community has focus on brain, and therefore the first preprocessing step is often to perform a brain mask, throwing out everything outside the brain. There is then a lack of tool for segmenting the skull, which is a tissue of interest for Paleoanthropology. We propose in this work to train a deep learning model for multiple tissue segmentations of the whole head. The task objective is to segment the MRI input volume in different labels. We keep the usual labels: GM (gray ma... |
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