Real-time corneal image segmentation for cataract surgery based on detection framework.

Objective: Cataract surgery is among the most frequently performed procedures worldwide. Accurate, real-time segmentation of the cornea and surgical instruments is vital for intraoperative guidance and surgical education. However, most existing deep learning-based segmentation methods depend on pixel-level annotations, which are time-consuming and limit practical deployment.

Methods: We present EllipseNet, an anchor-free framework utilizing ellipse-based modeling for real-time corneal segmentation in cataract surgery. Built upon the Hourglass network for feature extraction, EllipseNet requires only simple rectangular bounding box annotations from users. It then autonomously infers the major and minor axes of the corneal ellipse, generating elliptical bounding boxes that more precisely match corneal shapes.

Results: EllipseNet achieves efficient real-time performance by segmenting each image within 42 ms and attaining a Dice accuracy of 95.81%. It delivers segmentation speed nearly three times faster than state-of-the-art models, while maintaining similar accuracy levels.

Conclusion: EllipseNet provides rapid and accurate corneal segmentation in real time, significantly reducing annotation workload for practitioners. Its design streamlines the segmentation pipeline, lowering the barrier for clinical application. The source code is publicly available at: https://github.com/shixueyi/corneal-segmentation .