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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Mixture model-based approach for optic cup segmentation.

N M Tan¹, J Liu, D K Wong

¹Institute for Infocomm Research, A*STAR, Singapore. nmtan@i2a.a-star.edu.sg

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

Glaucoma detection is improved with ARGALI, an automated system assessing the optic cup-to-disc ratio in retinal images to identify optic nerve damage. This computer-aided diagnosis aids in early glaucoma identification and prevention of blindness.

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Area of Science:

Ophthalmology
Medical Imaging
Computer-Aided Diagnosis

Background:

Glaucoma is a primary cause of irreversible blindness.
Optic nerve head damage is a key indicator of glaucoma.
Accurate assessment of the optic cup-to-disc ratio is crucial for diagnosis.

Purpose of the Study:

To introduce ARGALI, an automated system for glaucoma detection.
To evaluate the efficacy of ARGALI in assessing the optic cup-to-disc ratio.
To provide a computer-aided diagnosis tool for early glaucoma identification.

Main Methods:

Development of the ARGALI system for automated image analysis.
Implementation of algorithms to determine optic cup and disc boundaries.
Utilizing optic cup-to-disc ratio for glaucoma screening.

Main Results:

ARGALI automates the measurement of the optic cup-to-disc ratio.
The system provides a quantitative assessment for glaucoma diagnosis.
Potential for improved efficiency and accuracy in clinical settings.

Conclusions:

ARGALI offers a promising automated approach for glaucoma detection.
Computer-aided assessment of optic nerve head parameters can aid ophthalmologists.
Early detection through systems like ARGALI can help prevent vision loss.