Published January 1, 2021
| Version v1
Journal article
Open
Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images
Creators
- 1. Koc Univ, Res Ctr Translat Med, Istanbul, Turkey
- 2. Techy Bilisim Ltd, Eskisehir, Turkey
- 3. Koc Univ, Sch Med, Dept Ophthalmol, Istanbul, Turkey
- 4. Koc Univ, Sch Med, Istanbul, Turkey
- 5. Yeditepe Univ, Dept Elect & Elect Engn, Istanbul, Turkey
Description
Purpose: In vivo confocal microscopy (IVCM) is a noninvasive, reproducible, and inexpensive diagnostic tool for corneal diseases. However, widespread and effortless image acquisition in IVCM creates serious image analysis workloads on ophthalmologists, and neural networks could solve this problem quickly. We have produced a novel deep learning algorithm based on generative adversarial networks (GANs), and we compare its accuracy for automatic segmentation of subbasal nerves in IVCM images with a fully convolutional neural network (U-Net) based method.
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