MT-NeT: Multi-Modality Transfer Learning Network for Automated Carotid Vessel Wall Segmentation using Sparse Annotation in MRI.
Xibao LiXi OuyangJiadong ZhangDongdong GuZehong CaoZhongxiang DingYiqiang ZhanXiang Sean ZhouZhong XueYuyao ZhangFeng ShiDinggang ShenPublished in: ISBI (2023)
Keyphrases
- transfer learning
- multi modality
- carotid artery
- vessel wall
- medical images
- magnetic resonance imaging
- fully automated
- active learning
- deformable models
- ultrasound images
- multi modal
- medical imaging
- machine learning
- magnetic resonance angiography
- magnetic resonance images
- region growing
- manual segmentation
- imaging modalities
- medical image analysis
- reinforcement learning
- information theoretic
- labeled data
- mutual information
- image intensity
- fully automatic
- mr images
- collaborative filtering
- contrast enhanced
- image segmentation
- image registration
- active shape model
- anatomical structures
- level set
- text classification
- semi supervised learning
- text categorization
- magnetic resonance
- medical data
- segmentation algorithm
- shape prior
- mri data
- computer assisted
- brain tumors
- computer aided
- intravascular ultrasound
- region of interest
- markov random field
- high dimensional
- similarity measure
- learning algorithm