Cargando…
Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images
To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MR...
Autores principales: | Hwang, Kihwan, Park, Juntae, Kwon, Young-Jae, Cho, Se Jin, Choi, Byung Se, Kim, Jiwon, Kim, Eunchong, Jang, Jongha, Ahn, Kwang-Sung, Kim, Sangsoo, Kim, Chae-Yong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782766/ https://www.ncbi.nlm.nih.gov/pubmed/36547492 http://dx.doi.org/10.3390/jimaging8120327 |
Ejemplares similares
-
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI
por: Laukamp, Kai Roman, et al.
Publicado: (2018) -
Experience with 7.0 T MRI in Patients with Supratentorial Meningiomas
por: Song, Sang Woo, et al.
Publicado: (2016) -
Clinical Management of Supratentorial Non-Skull Base Meningiomas
por: Adekanmbi, Adefisayo, et al.
Publicado: (2022) -
Fully automated condyle segmentation using 3D convolutional neural networks
por: Jha, Nayansi, et al.
Publicado: (2022) -
Extensive Supratentorial Hemorrhages Following Posterior Fossa Meningioma Surgery
por: Agrawal, Amit, et al.
Publicado: (2010)