Cargando…
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling
Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been...
Autores principales: | Valverde, Sergi, Oliver, Arnau, Roura, Eloy, Pareto, Deborah, Vilanova, Joan C., Ramió-Torrentà, Lluís, Sastre-Garriga, Jaume, Montalban, Xavier, Rovira, Àlex, Lladó, Xavier |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4644250/ https://www.ncbi.nlm.nih.gov/pubmed/26740917 http://dx.doi.org/10.1016/j.nicl.2015.10.012 |
Ejemplares similares
-
Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation
por: González-Villà, Sandra, et al.
Publicado: (2017) -
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
por: Valverde, Sergi, et al.
Publicado: (2018) -
Automated Detection of Lupus White Matter Lesions in MRI
por: Roura, Eloy, et al.
Publicado: (2016) -
A white matter lesion-filling approach to improve brain tissue volume measurements
por: Valverde, Sergi, et al.
Publicado: (2014) -
A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis
por: Salem, Mostafa, et al.
Publicado: (2017)