Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning.
Theodore Spaide1,2,3, Anand E Rajesh1,2, Nayoon Gim1,2,4
1Department of Ophthalmology, University of Washington, Seattle, Washington.
Ophthalmology Science
|October 9, 2024
Summary
Bayesian deep learning models improved geographic atrophy (GA) segmentation accuracy and provided uncertainty estimates. These methods enhance model trustworthiness and aid clinical decision-making in age-related macular degeneration.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Geographic atrophy (GA) is a leading cause of vision loss in age-related macular degeneration (AMD).
- Accurate segmentation of GA lesions is crucial for monitoring disease progression and evaluating treatment efficacy.
- Deep learning models show promise for automated GA segmentation, but quantifying prediction uncertainty remains a challenge.
Purpose of the Study:
- To apply and evaluate Bayesian deep learning techniques for quantifying uncertainty in semantic segmentation of geographic atrophy (GA).
- To compare the performance of Bayesian methods (Monte Carlo dropout, ensemble) against a traditional deep learning model for GA segmentation.
Main Methods:
- Retrospective analysis of spectral-domain optical coherence tomography (SD-OCT) images from the SWAGGER cohort.
- Development of two approximate Bayesian deep learning models: Monte Carlo dropout and ensemble methods.
- Comparison of model performance using Dice scores and calculation of pixel-wise uncertainty using Shannon Entropy.
Main Results:
- Bayesian deep learning models demonstrated significantly higher Dice scores (MC dropout: 0.90, ensemble: 0.88) compared to the traditional model (0.82).
- Both Bayesian methods produced a greater number of pixels with high entropy, indicating higher uncertainty estimates.
- The models provided pixel-wise estimates of uncertainty for GA segmentation.
Conclusions:
- Quantifying prediction uncertainty in GA segmentation enhances model trustworthiness for clinical applications.
- Bayesian deep learning techniques improve segmentation performance and provide valuable uncertainty measures, aiding clinician decision-making.
- These advanced methods offer a more robust approach to analyzing GA in OCT imaging.
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