-Net: 3D Fully Convolution Network-Based Vertebrae Segmentation from CT Spinal Images.
Wenhui ZhouLili LinGuangtao GePublished in: Int. J. Pattern Recognit. Artif. Intell. (2019)
Keyphrases
- mri images
- image slices
- ct and mr images
- segmentation method
- medical images
- segmentation algorithm
- three dimensional
- test images
- image analysis
- spinal cord
- magnetic resonance images
- medical imaging
- magnetic resonance
- mri brain
- edge detection
- fully automatic
- computer tomography
- lymph nodes
- ct data
- input image
- magnetic resonance imaging
- segmentation errors
- image regions
- computerized tomography
- image processing
- microscopy images
- image segmentation
- mr images
- image features
- ground truth
- segmented images
- image data
- accurate segmentation
- white blood
- brain mri
- image registration
- automatically segmenting
- brain tumors
- x ray images
- ct images
- inter patient
- multiscale
- level set
- x ray
- contrast enhanced
- automatic segmentation
- automated segmentation
- deformable models
- image reconstruction
- dual energy
- articulated model
- medical image registration
- ct volume
- region of interest
- statistical shape model
- patient specific
- lung parenchyma
- intraoperative