Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation
Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentati...
- Autores:
-
Sanchez, Sergio
Vallez, Noelia
Bueno, Gloria
Marrugo, Andres G
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12773
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12773
- Palabra clave:
- Data augmentation
Warping transforms
Corneal endothelium
Semi-supervised segmentation
Deep convolutional neural networks (CNNs)
Image segmentation
Medical imaging
Keypoint extraction
Delaunay triangulation
Affine transformations
Mask refinement Mean intersection over union (mIoU)
Dice coefficient (DC)
Natural variability
Medical image cell segmentation
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/