Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

Automatic segmentation of optic disk (OD) and cup regions in fundus images is essential in deriving clinical parameters, such as, cup-to-disk ratio (CDR), to assist glaucoma diagnosis. This paper presents a deep learning system using fully convolutional neural networks (FCN) to perform such segmenta...

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Bibliographic Details
Published in:2018 25th IEEE International Conference on Image Processing (ICIP) pp. 2227 - 2231
Main Authors: Edupuganti, Venkata Gopal, Chawla, Akshay, Kale, Amit
Format: Conference Proceeding
Language:English
Published: IEEE 01-10-2018
Subjects:
Deep Learning
Fundus Imaging
Glaucoma
Image segmentation
Machine learning
Neural networks
Optical computing
Optical imaging
Semantic Segmentation
Semantics
Training
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Summary:Automatic segmentation of optic disk (OD) and cup regions in fundus images is essential in deriving clinical parameters, such as, cup-to-disk ratio (CDR), to assist glaucoma diagnosis. This paper presents a deep learning system using fully convolutional neural networks (FCN) to perform such segmentation, discusses various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions while training the neural network. Experimental evaluations on Drishti-GS dataset demonstrate that the presented method achieves comparable and superior F-score to prior work on optic disk and cup segmentation, respectively.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451753