Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network
Document Type
Article
Publication Date
1-1-2018
Department
Department of Biomedical Engineering
Abstract
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing
Publication Title
Journal of Healthcare Engineering
Recommended Citation
Cui, S.,
Mao, L.,
Jiang, J.,
Liu, C.,
&
Xiong, S.
(2018).
Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network.
Journal of Healthcare Engineering,
2018, 4940593.
http://doi.org/10.1155/2018/4940593
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1292