DeepThalamus: A novel deep learning method for automatic segmentation of
brain thalamic nuclei from multimodal ultra-high resolution MRI
- URL: http://arxiv.org/abs/2401.07751v1
- Date: Mon, 15 Jan 2024 14:59:56 GMT
- Title: DeepThalamus: A novel deep learning method for automatic segmentation of
brain thalamic nuclei from multimodal ultra-high resolution MRI
- Authors: Marina Ruiz-Perez, Sergio Morell-Ortega, Marien Gadea, Roberto
Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Mariam de la Iglesia-Vaya,
Thomas Tourdias, Pierrick Coup\'e and Jos\'e V. Manj\'on
- Abstract summary: We have designed and implemented a multimodal volumetric deep neural network for the segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3)
A database of semiautomatically segmented thalamic nuclei was created using ultra-high resolution T1, T2 and White Matter nulled (WMn) images.
A novel Deep learning based strategy was designed to obtain the automatic segmentations and trained to improve its robustness and accuaracy.
- Score: 32.73124984242397
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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