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Updated: Jun 15, 2025
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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
Published on: February 9, 2019
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Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume
Haochen Zhang1, Anna Heinke2, Carlo Miguel B Galang2
1Electrical and Computer Engineering Department, UC San Diego.
... IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision
|August 23, 2024
Summary
Deep learning classifiers using Optical Coherence Tomography Angiography (OCTA) can accurately grade Age-related Macular Degeneration (AMD). Analyzing 3D OCTA volumes directly improves accuracy, outperforming human experts in AMD staging.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Age-related Macular Degeneration (AMD) causes central vision loss.
- Optical Coherence Tomography Angiography (OCTA) visualizes pathogenic vessels in AMD.
- Current diagnostic methods can be limited.
Purpose of the Study:
- To evaluate OCTA's effectiveness for AMD stage grading using deep learning.
- To develop a robust deep learning model for AMD classification from OCTA data.
- To compare AI performance against human experts in AMD staging.
Main Methods:
- Developed a 2D classifier using OCTA projections.
- Identified segmentation errors impacting classification accuracy.
- Proposed a 3D OCTA volume analysis using a 2D CNN with projection supervision.
Main Results:
- Achieved over 80% accuracy in a four-stage AMD grading task.
- The 3D volume analyzer demonstrated robustness with segmentation errors.
- AI performance significantly surpassed the 60% accuracy of human experts.
Conclusions:
- OCTA contains sufficient information for accurate AMD stage grading.
- Direct 3D OCTA volume analysis enhances classification robustness and accuracy.
- Deep learning models show promise for objective and precise AMD assessment.
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