Cargando…
A Robust and Accurate Deep-learning-based Method for the Segmentation of Subcortical Brain: Cross-dataset Evaluation of Generalization Performance
PURPOSE: To analyze subcortical brain volume more reliably, we propose a deep learning segmentation method of subcortical brain based on magnetic resonance imaging (MRI) having high generalization performance, accuracy, and robustness. METHODS: First, local images of three-dimensional (3D) bounding...
Autores principales: | Furuhashi, Naoya, Okuhata, Shiho, Kobayashi, Tetsuo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Japanese Society for Magnetic Resonance in Medicine
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203473/ https://www.ncbi.nlm.nih.gov/pubmed/32389928 http://dx.doi.org/10.2463/mrms.mp.2019-0199 |
Ejemplares similares
-
Characterization of the Growth of Deep and Subcortical White Matter Hyperintensity on MR Imaging: A Retrospective Cohort Study
por: Adachi, Michito, et al.
Publicado: (2017) -
Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models’ Performance and Robustness
por: Sanaat, Amirhossein, et al.
Publicado: (2022) -
An attention-based context-informed deep framework for infant brain subcortical segmentation
por: Chen, Liangjun, et al.
Publicado: (2023) -
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization
por: Baniasadi, Mehri, et al.
Publicado: (2022) -
Accurate segmentation of neonatal brain MRI with deep learning
por: Richter, Leonie, et al.
Publicado: (2022)