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

Citation Author(s):
Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale
Submitted by:
Akshay Chawla
Last updated:
8 October 2018 - 7:18pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Akshay Chawla
Paper Code:
2534
 

• To automatically segment optic disk (OD) and cup regions in fundus images to derive clinical parameters, such as, cup-to-disk diameter ratio (CDR), to assist glaucoma diagnosis. An eye fundus camera is non-invasive and low-cost,
enabling the screening of a large number of patients quickly.

• Discuss various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions during network optimization.

• Evaluate proposed approaches on the Drishti-GS dataset.

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