Experimental eye research · 2026
Geometric Model for Semi-Automatic Foveal Localization Based on the Optic Nerve Head-Fovea Axis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Quarta Alberto, Chujo Shinichiro, Forte Paolo, Huang Jianfeng, Feo Alessandro, Abbasgholizadeh Rouzbeh, Soylu Ceren, Sivilotti Federico, Lee Seungmin, Velaga Swetha Bindu, Nittala Muneeswar G, Corradetti Giulia, Sadda Srinivas R
Аннотация
PURPOSE: To evaluate the accuracy of a semi-automatic foveal localization method based on optic nerve head (ONH)-fovea geometry.
METHODS: This cross-sectional validation study included 46 eyes. For each image, the foveal center was manually selected before optic nerve head (ONH) segmentation to provide a partially blinded reference standard for the semi-automatic method. The primary endpoint was the median optimized localization error, tested against a prespecified 212 μm Euclidean threshold. Secondary outcomes included distance-based success rate, Bland-Altman directional bias, optimized angle analysis, and overlap-based grading using paired 150 μm central-foveal rings. Grade 3 indicated optimal localization, with the model-derived point within 50 μm of the manual reference; Grade 2 indicated at least 50% ring overlap; Grade 1 indicated at least 33% ring overlap; and Grade 0 indicated less than 33% overlap or error exceeding 212 μm.
RESULTS: The median optimized localization error was 100 μm, with a bootstrap 95% confidence interval of 78-144 μm. The interquartile range was 56-211 μm, and the mean ± standard deviation was 126 ± 90 μm. The median error was significantly below the 212 μm clinical threshold (p < 0.001). Overall, 36 of 46 eyes (78.3%; 95% Wilson CI, 64-88%) had an error below 212 μm. Using the 150 μm grading ring, 33 of 46 eyes (71.7%) achieved Grade ≥1, and 24 eyes (52.2%) achieved Grade ≥2. The optimized angle was 6.37° ± 0.45°.
CONCLUSION: In normal eyes, the semi-automatic ONH-fovea geometric model localized the fovea in 30° infrared images with clinically acceptable accuracy in most eyes. Evaluation in eyes with macular disease, where direct foveal localization may be challenging, is warranted as a next step.
METHODS: This cross-sectional validation study included 46 eyes. For each image, the foveal center was manually selected before optic nerve head (ONH) segmentation to provide a partially blinded reference standard for the semi-automatic method. The primary endpoint was the median optimized localization error, tested against a prespecified 212 μm Euclidean threshold. Secondary outcomes included distance-based success rate, Bland-Altman directional bias, optimized angle analysis, and overlap-based grading using paired 150 μm central-foveal rings. Grade 3 indicated optimal localization, with the model-derived point within 50 μm of the manual reference; Grade 2 indicated at least 50% ring overlap; Grade 1 indicated at least 33% ring overlap; and Grade 0 indicated less than 33% overlap or error exceeding 212 μm.
RESULTS: The median optimized localization error was 100 μm, with a bootstrap 95% confidence interval of 78-144 μm. The interquartile range was 56-211 μm, and the mean ± standard deviation was 126 ± 90 μm. The median error was significantly below the 212 μm clinical threshold (p < 0.001). Overall, 36 of 46 eyes (78.3%; 95% Wilson CI, 64-88%) had an error below 212 μm. Using the 150 μm grading ring, 33 of 46 eyes (71.7%) achieved Grade ≥1, and 24 eyes (52.2%) achieved Grade ≥2. The optimized angle was 6.37° ± 0.45°.
CONCLUSION: In normal eyes, the semi-automatic ONH-fovea geometric model localized the fovea in 30° infrared images with clinically acceptable accuracy in most eyes. Evaluation in eyes with macular disease, where direct foveal localization may be challenging, is warranted as a next step.