| Autor: |
Theodore Spaide, PhD, Anand E. Rajesh, MD, Nayoon Gim, Marian Blazes, MD, Cecilia S. Lee, MD, MS, Niranchana Macivannan, PhD, Gary Lee, PhD, MEng, Warren Lewis, MS, Ali Salehi, PhD, Luis de Sisternes, PhD, Gissel Herrera, MD, Mengxi Shen, MD, PhD, Giovanni Gregori, PhD, Philip J. Rosenfeld, MD, PhD, Varsha Pramil, MD, MS, Nadia Waheed, MD, MPH, Yue Wu, PhD, Qinqin Zhang, PhD, Aaron Y. Lee, MD, MSCI |
| Jazyk: |
angličtina |
| Rok vydání: |
2025 |
| Předmět: |
|
| Zdroj: |
Ophthalmology Science, Vol 5, Iss 1, Pp 100587- (2025) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9145 |
| DOI: |
10.1016/j.xops.2024.100587 |
| Popis: |
Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study. Methods: The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model. Main Outcome Measures: Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy. Results: The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87–0.93) and the ensemble method (0.88, 95% confidence interval 0.85–0.91) were significantly higher (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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