Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Endothelial Images
2.2. Training Set Preparation
- Cell border class finds pixel which corresponds to cell border and then cuts the sample assuring that this pixel is placed as a central point of the patch.
- Cell center class for each cell finds its mass-center and these coordinates are used to choose an appropriate pixel as a central point of the sample.
- Cell body is an additional class, which assumes to describe data which are far from cell center but are not a border. In order to create the sample, which belongs to this set, a cell border was sampled, and points laying 5 pixels from it in a horizontal or vertical direction (chosen randomly) were denoted as a central point of a sample.
2.3. Convolutional Neural Network
2.4. Segmentation Approaches
2.5. Cell Splitting
2.6. Border Skeletonization
3. Results and Discussion
3.1. CNN for Two-Class Problem
3.2. CNN for Three-Class Problem
3.3. Splitting Merged Cells
3.4. Segmentation Accuracy
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Approach | I | II | III |
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Dice index | |||
Jaccard coefficient | |||
MHD | |||
FOM | |||
Yasnoff | |||
Gavet |
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Nurzynska, K. Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images. Symmetry 2018, 10, 60. https://doi.org/10.3390/sym10030060
Nurzynska K. Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images. Symmetry. 2018; 10(3):60. https://doi.org/10.3390/sym10030060
Chicago/Turabian StyleNurzynska, Karolina. 2018. "Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images" Symmetry 10, no. 3: 60. https://doi.org/10.3390/sym10030060