Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with signal intensity correction.
Hyeonjin KimHelen HongDae Chul JungKidon ChangKoon Ho RhaPublished in: Medical Imaging: Computer-Aided Diagnosis (2021)
Keyphrases
- mr images
- accurate segmentation
- convolutional neural network
- medical images
- manual segmentation
- automated segmentation
- brain mr images
- magnetic resonance
- partial volume
- face detection
- brain tumors
- mr brain
- automatic segmentation
- brain structures
- intensity inhomogeneity
- magnetic resonance images
- dice similarity coefficient
- cardiac magnetic resonance
- bias field
- mr imaging
- contrast enhanced
- nonrigid registration
- mr brain images
- intensity distribution
- tissue segmentation
- mri data
- image segmentation
- image data
- phantom images
- inhomogeneity correction
- segmentation algorithm
- brain tissue
- medical imaging
- model based segmentation
- brain mri
- quantitative measurements
- fully automatic
- imaging modalities
- object detection
- computer aided diagnosis
- low signal to noise ratio
- carotid artery
- lesion segmentation
- level set
- prostate cancer
- inter patient
- computer vision
- anatomical structures
- atlas based segmentation
- segmentation method
- ct data
- clinical applications
- medical image analysis
- quantitative evaluation
- region growing
- image analysis
- image intensity
- white matter