RGA-Unet: An improved U-net segmentation model based on residual grouped convolution and convolutional block attention module for brain tumor MRI image segmentation.
Siyi XunYan ZhangSixu DuanHuachao ChenMingwei WangJiangang ChenTao TanPublished in: CSSE (2022)
Keyphrases
- brain tumors
- image segmentation
- magnetic resonance images
- mr images
- tumor segmentation
- mri images
- deformable registration
- mri scans
- magnetic resonance
- low grade gliomas
- medical imaging
- brain mr images
- medical image analysis
- brain tissue
- segmentation algorithm
- image processing
- medical images
- segmentation method
- segmentation result
- image analysis
- model based segmentation
- level set
- tumor growth
- image segmentation algorithm
- texture segmentation
- brain images
- gray level
- deformable models
- multiscale
- magnetic resonance imaging
- region growing
- fully unsupervised
- active contours
- energy functional
- visual attention
- manual segmentation
- brain tumor segmentation
- level set method
- markov random field
- graph cuts
- mr imaging
- shape model
- anatomical structures
- brain scans
- magnetic resonance spectroscopy
- otsu method