Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography.
Yuexing HanXiaolong LiBing WangLu WangPublished in: Algorithms (2021)
Keyphrases
- computed tomography
- contrast enhanced
- ct images
- liver segmentation
- medical images
- medical imaging
- convolutional neural networks
- ct scans
- ct data
- treatment planning
- image reconstruction
- computed tomography scans
- pet ct
- lymph nodes
- three dimensional
- medical image processing
- computer tomography
- fully automatic
- fracture detection
- medical image segmentation
- lung cancer patients
- brain tumors
- image segmentation
- image processing
- pet images
- mr images
- computer aided diagnosis
- medical image analysis
- inter observer variability
- radio frequency ablation
- cone beam
- anatomical structures
- magnetic resonance images
- pre operative
- image analysis
- magnetic resonance
- image registration
- level set
- magnetic resonance imaging
- object boundaries
- x ray
- region of interest
- segmentation algorithm
- deformable models
- multiscale
- graph cuts
- automatic segmentation