3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
URI: https://gptkb.org/entity/3D_U-Net:_Learning_Dense_Volumetric_Segmentation_from_Sparse_Annotation
GPTKB entity
Statements (22)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic_journal
|
| gptkbp:application |
biomedical volumetric data segmentation
|
| gptkbp:architecture |
gptkb:convolutional_neural_network
|
| gptkbp:author |
gptkb:Thomas_Brox
gptkb:Olaf_Ronneberger gptkb:Özgün_Çiçek gptkb:Ahmed_Abdulkadir gptkb:Soeren_S._Lienkamp |
| gptkbp:citation |
high
|
| gptkbp:extendsTo |
gptkb:U-Net_architecture
|
| gptkbp:field |
deep learning
medical image analysis |
| gptkbp:focusesOn |
volumetric segmentation
sparse annotation |
| gptkbp:hasMethod |
gptkb:3D_U-Net
|
| https://www.w3.org/2000/01/rdf-schema#label |
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
|
| gptkbp:influenced |
medical image segmentation research
|
| gptkbp:openSource |
available
|
| gptkbp:publicationYear |
2016
|
| gptkbp:publishedIn |
Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2016
|
| gptkbp:bfsParent |
gptkb:3D_U-Net
|
| gptkbp:bfsLayer |
7
|