Active Learning for Meibomian Gland Segmentation in Infrared Meibography Images

Le résumé fourni par la source

Meibomian Gland Dysfunction (MGD) is a key contributor to clinical Dry Eye Disease (DED), and accurate meibomian gland segmentation is essential for its intelligent diagnosis. However, the high cost of annotation remains a major barrier to improving segmentation quality. Active Learning (AL), which selects the most informative samples for annotation, offers an efficient solution. This study explores the application of AL to infrared meibography image segmentation and introduces a novel progressive hybrid sampling framework. Initial AL stages often rely on random sampling for initial labeled sets, risking unstable performance from uninformative data and cold starts. Additionally, meibography images always have specular reflections whose late introduction in training degrades future segmentation accuracy. To address these challenges, we incorporate prior knowledge of specular reflections into the initial labeled set construction, enabling the model to handle such artifacts from the beginning. We then implement a two-stage dynamic sampling strategy: entropy-based uncertainty sampling is used in the early iterations to maximize annotated data informativeness and rapidly boost model performance. However, as AL progresses, only focusing on uncertainty may overlook global data distribution and lead to redundant annotations. Hence, an adaptive threshold is introduced to monitor sample redundancy. When redundancy exceeds this threshold, a diversity sampling module is activated to improve model generalization. Experimental results on the public MGD-1K dataset show that our method achieves superior segmentation performance under the same annotation budget. Unlike mainstream methods that often fail to outperform random baselines, our approach consistently delivers accurate and efficient meibomian gland segmentation.

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