AWSnet: An auto-weighted supervision attention network for myocardial scar and edema segmentation in multi-sequence cardiac magnetic resonance images.
Kai-Ni WangXin YangJuzheng MiaoLei LiJing YaoPing ZhouWufeng XueGuang-Quan ZhouXiahai ZhuangDong NiPublished in: Medical Image Anal. (2022)
Keyphrases
- magnetic resonance images
- myocardial motion
- brain mri
- medical images
- brain tumors
- cardiac mri
- partial volume
- mr images
- medical imaging
- intra subject
- tissue segmentation
- brain scans
- mri images
- multiple sclerosis lesions
- brain structures
- mri data
- accurate segmentation
- left ventricular
- magnetic resonance imaging
- bias field
- white matter
- mr imaging
- cardiac mr images
- myocardial infarction
- short axis
- magnetic resonance
- brain tumor segmentation
- intensity distribution
- manual segmentation
- tagged mri
- level set
- brain tissue
- medical image analysis
- imaging modalities
- phase contrast
- high resolution
- deformable models
- left ventricle
- anatomical structures
- image segmentation
- image analysis
- segmentation algorithm
- image registration
- gray matter
- medical diagnosis
- corpus callosum
- diffusion tensor
- image processing
- diffusion weighted
- partial volume effects
- cardiac motion
- x ray
- fully automatic
- shape prior
- pattern recognition
- brain mr images
- computer vision