Automatic liver segmentation in abdominal CT images using combined 2.5D and 3D segmentation networks with high-score shape prior for radiotherapy treatment planning.
Julip JungHelen HongTae-Sik JeongJinsil SeongJin Sung KimPublished in: Medical Imaging: Computer-Aided Diagnosis (2020)
Keyphrases
- treatment planning
- shape prior
- ct images
- accurate segmentation
- deformable models
- level set
- segmentation result
- medical images
- image segmentation
- graph cuts
- segmentation method
- active shape model
- lymph nodes
- shape model
- object boundaries
- image guided
- computer aided
- active contours
- automated segmentation
- fully automatic
- ct scans
- segmentation algorithm
- ct data
- radiation therapy
- medical imaging
- computed tomography
- shape variations
- energy function
- medical image analysis
- clinical applications
- user interaction
- prior information
- active contour model
- semi automatic
- imaging modalities
- intensity distribution
- statistical models
- curve evolution
- energy functional
- medical data
- automatic segmentation
- global optimization
- bayesian framework
- prior knowledge
- medical image processing
- statistical shape model
- region growing
- magnetic resonance
- low contrast
- level set method
- shape representation
- mr images
- markov random field
- three dimensional