Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection.
Takaaki SuginoTaichi KinNobuhito SaitoYoshikazu NakajimaPublished in: Int. J. Comput. Assist. Radiol. Surg. (2024)
Keyphrases
- mr images
- accurate segmentation
- basal ganglia
- medical images
- convolutional neural network
- brain mr images
- magnetic resonance
- partial volume
- magnetic resonance images
- brain tumors
- brain structures
- mr brain
- intensity inhomogeneity
- mr imaging
- dice similarity coefficient
- bias field
- cardiac magnetic resonance
- human brain
- mr brain images
- intensity distribution
- manual segmentation
- nonrigid registration
- mri data
- action selection
- contrast enhanced
- inhomogeneity correction
- atlas construction
- tissue segmentation
- brain mri
- connectionist models
- model based segmentation
- phantom images
- inter patient
- gray matter
- neural network
- prostate cancer
- image intensity
- medical imaging
- medical image analysis
- image data
- level set
- brain tissue
- information processing
- automated segmentation
- anatomical structures
- fully automatic
- magnetic resonance imaging
- deformable registration
- lesion segmentation
- segmentation algorithm
- image segmentation
- carotid artery
- machine learning