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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