Esophagus segmentation from planning CT images using an atlas-based deep learning approach.
João Otávio Bandeira DinizJonnison Lima FerreiraPedro Henrique Bandeira DinizAristófanes Corrêa SilvaAnselmo Cardoso de PaivaPublished in: Comput. Methods Programs Biomed. (2020)
Keyphrases
- ct images
- deep learning
- medical images
- medical imaging
- medical image analysis
- liver segmentation
- ct scans
- pet ct
- lung nodules
- computed tomography
- fracture detection
- segmentation method
- automated segmentation
- anatomical structures
- image segmentation
- medical image segmentation
- unsupervised learning
- machine learning
- magnetic resonance imaging
- region of interest
- deformable models
- image analysis
- ground glass opacity
- image processing
- mental models
- segmentation algorithm
- x ray
- level set
- shape prior
- image registration
- lymph nodes
- lung parenchyma
- weakly supervised
- computer aided diagnosis
- magnetic resonance images
- brain images
- vessel segmentation
- fully automatic
- mr images
- multiscale
- object recognition
- computer vision