Cargando…
Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity, with Theory Reviewed
Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating suc...
Autores principales: | Huang, Yu, Parra, Lucas C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436344/ https://www.ncbi.nlm.nih.gov/pubmed/25992793 http://dx.doi.org/10.1371/journal.pone.0125477 |
Ejemplares similares
-
A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT
por: Arab, Ali, et al.
Publicado: (2020) -
Fully automated grey and white matter spinal cord segmentation
por: Prados, Ferran, et al.
Publicado: (2016) -
Fully automated segmentation of callus by micro-CT compared to biomechanics
por: Bissinger, Oliver, et al.
Publicado: (2017) -
Deep learning for the fully automated segmentation of the inner ear on MRI
por: Vaidyanathan, Akshayaa, et al.
Publicado: (2021) -
A Fully Automated Deep Learning Network for Brain Tumor Segmentation
por: Bangalore Yogananda, Chandan Ganesh, et al.
Publicado: (2020)