Segmentation of Vestibular Schwannoma from Multi-parametric Magnetic Resonance Images using Convolutional Neural Network.
Wei-Kai LeeChih-Chun WuTzu-Hsuan HuangChun-Yi LinCheng-Chia LeeWen-Yuh ChungPo-Shan WangChia-Feng LuHsiu-Mei WuWan-Yuo GuoYu-Te WuPublished in: DMIP (2019)
Keyphrases
- magnetic resonance images
- brain mri
- convolutional neural network
- medical images
- brain scans
- partial volume
- brain tumors
- medical imaging
- multiple sclerosis lesions
- tissue segmentation
- mr images
- intra subject
- brain structures
- bias field
- mri images
- mri brain
- white matter
- magnetic resonance imaging
- brain tissue
- mri data
- medical image analysis
- corpus callosum
- accurate segmentation
- high resolution
- face detection
- brain atlas
- gray matter
- intensity distribution
- multiscale
- mr imaging
- image segmentation
- brain mr images
- brain tumor segmentation
- three dimensional
- magnetic resonance
- parametric models
- segmentation method
- deformable models
- diffusion weighted
- cerebrospinal fluid
- anatomical structures
- intensity inhomogeneity
- object detection
- level set
- segmentation algorithm