Enhancing Corneal Endothelium Segmentation: A Comparison of Classification and Distance-Map Regression UNets
Author:
Affiliation:
1. Universidad Tecnológica de Bolívar,Facultad de Ingeniería,Cartagena,Colombia
2. Universidad Tecnológica de Bolívar,Facultad de Ciencias Básicas,Cartagena,Colombia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10334039/10334065/10334249.pdf?arnumber=10334249
Reference17 articles.
1. U-net: Convolutional networks for biomedical image segmentation;ronneberger;Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015 18th International Conference,2015
2. Automated segmentation of the corneal endothelium in a large set of ‘real-world’ specular microscopy images using the U-Net architecture
3. Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy
4. Convolution neural network for contour extraction of corneal endothelial cells;katafuchi;Thirteenth International Conference on Quality Control by Artificial Vision 2017,2017
5. Comeal endothelium cell segmentation and count using k-means and watershed algorithms;kumar;2018 Second International Conference on Advances in Electronics Computers and Communications (ICAECC),2018
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