short-paper

Optic Disc and Cup Segmentation Based on Enhanced SegNet

Authors:

Jinman KimAuthors Info & Claims

CASA '19: Proceedings of the 32nd International Conference on Computer Animation and Social Agents

Pages 33 - 36

https://doi.org/10.1145/3328756.3328774

Published: 01 July 2019 Publication History

Abstract

Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.

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Cited By

de Moura JNovo JOrtega MBarreira NPenedo M(2021)Automated Segmentation of the Central Serous Chorioretinopathy fluid regions using Optical Coherence Tomography Scans2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS52027.2021.00008(1-6)Online publication date: Jun-2021
https://doi.org/10.1109/CBMS52027.2021.00008
Lu SHu MLi RXu Y(2020)A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect SegmentationIEEE Access10.1109/ACCESS.2020.30094428(132348-132359)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3009442

Index Terms

Optic Disc and Cup Segmentation Based on Enhanced SegNet
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Computer graphics
    1. Image manipulation

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cover image ACM Other conferences

CASA '19: Proceedings of the 32nd International Conference on Computer Animation and Social Agents

July 2019

95 pages

ISBN:9781450371599

DOI:10.1145/3328756

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
EUROGRAPHICS: The European Association for Computer Graphics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2019

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Short-paper
Research
Refereed limited

Funding Sources

Fundo para o Desenvolvimento das Ciências e da Tecnologia
Science and Technology Commission of Shanghai Municipality
National Natural Science Foundation of China
National Key Research and Development Program of China

Conference

CASA '19

CASA '19: Computer Animation and Social Agents

July 1 - 3, 2019

Paris, France

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Overall Acceptance Rate 18 of 110 submissions, 16%

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Cited By

de Moura JNovo JOrtega MBarreira NPenedo M(2021)Automated Segmentation of the Central Serous Chorioretinopathy fluid regions using Optical Coherence Tomography Scans2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS52027.2021.00008(1-6)Online publication date: Jun-2021
https://doi.org/10.1109/CBMS52027.2021.00008
Lu SHu MLi RXu Y(2020)A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect SegmentationIEEE Access10.1109/ACCESS.2020.30094428(132348-132359)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3009442

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