OFF-eNET: An Optimally Fused Fully End-to-End Network for Automatic Dense Volumetric 3D Intracranial Blood Vessels Segmentation.
Anam NazirMuhammad Nadeem CheemaBin ShengHuating LiPing LiPo YangYounhyun JungJing QinJinman KimDavid Dagan FengPublished in: IEEE Trans. Image Process. (2020)
Keyphrases
- end to end
- blood vessels
- vessel segmentation
- congestion control
- tubular structures
- wireless ad hoc networks
- retinal vessel
- high bandwidth
- internet protocol
- optic disc
- low contrast
- fundus images
- vessel detection
- transport layer
- fully automatic
- retinal images
- packet loss rate
- rate adaptation
- medical imagery
- admission control
- ad hoc networks
- segmentation algorithm
- multiscale
- differentiated services
- image segmentation
- medical images
- region growing
- region of interest
- segmentation method
- scalable video
- wireless sensor networks
- peer to peer
- network traffic
- phase contrast
- image quality
- computer networks
- network architecture
- network management
- volumetric data
- network resources
- brain tumors