JAMA · 2023
Screening and Preventive Interventions for Oral Health in Adults: US Preventive Services Task Force Recommendation Statement.
СтатьяКомментарийНазвание и аннотация: Перевод пока не подготовлен
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US Preventive Services Task Force, Barry Michael J, Nicholson Wanda K, Silverstein Michael, Chelmow David, Coker Tumaini Rucker, Davis Esa M, Donahue Katrina E, Jaén Carlos Roberto, Li Li, Ogedegbe Gbenga, Pbert Lori, Rao Goutham, Ruiz John M, Stevermer James, Tsevat Joel, Underwood Sandra Millon, Wong John B
Аннотация
PURPOSE: The purpose of this study was to evaluate a semi-supervised Mean Teacher (MT) framework for semantic segmentation of cataract surgical images, addressing the challenge of limited labeled data in real-world clinical applications.
METHODS: We adapted the MT framework for four-class segmentation of the iris, pupil, intraocular lens, and surgical instruments using a small labeled set and 40,000 unlabeled Cataract-1K frames. Performance was assessed using Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95). MT was compared with a fully supervised UNet under varying labeled and unlabeled conditions, and additional baselines provided broader context. Ablation studies evaluated noise types and key hyperparameters, including consistency weight (λ) and exponential moving average (EMA) decay (α). Internal validation used Cataract-1K, and external testing was done on CaDIS and CatInstSeg.
RESULTS: Our MT model consistently outperformed the supervised UNet baseline. With 100 labeled images and 40,000 unlabeled frames, MT achieved a DSC of 0.76 ± 0.12 compared to UNet with 0.59 ± 0.17 (t = 21.23, P < 0.05). On CaDIS and CatInstSeg, MT reached DSCs of 0.69 ± 0.17 and 0.71 ± 0.20, outperforming UNet at 0.62 ± 0.08 and 0.64 ± 0.23. Optimal performance was observed with λ = 0.1, α = 0.995, and Gaussian noise σ = 15.
CONCLUSIONS: The MT framework provides an effective semi-supervised solution for surgical image segmentation with limited annotations. Full source code and utility scripts will be released upon acceptance at: https://github.com/mahtabfaraji1/Semi-supervised-segmentation-of-cataract-surgical-images.
TRANSLATIONAL RELEVANCE: Accurate segmentation of ocular anatomy and instruments supports surgical guidance, intraoperative decision making, and training tools in data-limited clinical environments.
METHODS: We adapted the MT framework for four-class segmentation of the iris, pupil, intraocular lens, and surgical instruments using a small labeled set and 40,000 unlabeled Cataract-1K frames. Performance was assessed using Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95). MT was compared with a fully supervised UNet under varying labeled and unlabeled conditions, and additional baselines provided broader context. Ablation studies evaluated noise types and key hyperparameters, including consistency weight (λ) and exponential moving average (EMA) decay (α). Internal validation used Cataract-1K, and external testing was done on CaDIS and CatInstSeg.
RESULTS: Our MT model consistently outperformed the supervised UNet baseline. With 100 labeled images and 40,000 unlabeled frames, MT achieved a DSC of 0.76 ± 0.12 compared to UNet with 0.59 ± 0.17 (t = 21.23, P < 0.05). On CaDIS and CatInstSeg, MT reached DSCs of 0.69 ± 0.17 and 0.71 ± 0.20, outperforming UNet at 0.62 ± 0.08 and 0.64 ± 0.23. Optimal performance was observed with λ = 0.1, α = 0.995, and Gaussian noise σ = 15.
CONCLUSIONS: The MT framework provides an effective semi-supervised solution for surgical image segmentation with limited annotations. Full source code and utility scripts will be released upon acceptance at: https://github.com/mahtabfaraji1/Semi-supervised-segmentation-of-cataract-surgical-images.
TRANSLATIONAL RELEVANCE: Accurate segmentation of ocular anatomy and instruments supports surgical guidance, intraoperative decision making, and training tools in data-limited clinical environments.