# Dual U-Net with multi-task attention for automated eyelid curvature quantification

## TL;DR

This paper introduces a new AI method to automatically measure eyelid curvature from eye images, offering a reliable and accurate tool for ophthalmic diagnosis and surgery planning.

## Contribution

A dual-branch U-Net with multi-task attention is proposed for simultaneous segmentation and curvature quantification of eyelid margins.

## Key findings

- The AtDU-Net model achieved an IoU of 0.979 and a Dice coefficient of 0.989 in segmentation tasks.
- Automated curvature measurements showed strong correlation with manual annotations (0.9032 for upper and 0.9154 for lower eyelid).
- Over 92% of samples fell within Bland-Altman agreement limits, confirming measurement consistency and reliability.

## Abstract

Eyelid curvature analysis serves as a key morphological indicator in the diagnosis of ophthalmic diseases and postoperative evaluation. This study aims to develop an automated and reproducible image processing method to accurately extract eyelid margin curves from anterior segment images and perform quantitative curvature analysis.

A dual-branch U-Net architecture is proposed, utilizing a shared encoder and task-specific decoders to simultaneously segment the palpebral fissure and corneal regions. Based on the segmentation results, eyelid margin curves were extracted and fitted with second-order polynomials to calculate curvature values.

A total of 130 anterior segment images were collected. In segmentation tasks, the proposed AtDU-Net model achieved an IoU of 0.979 and a Dice coefficient of 0.989. The automatically measured eyelid curvatures showed high consistency with manual annotations, with correlation coefficients of 0.9032 for the upper eyelid and 0.9154 for the lower eyelid. Bland-Altman analysis indicated that over 92% of the samples fell within the limits of agreement, validating the consistency and reliability of the measurements.

The proposed method demonstrates superior performance in terms of accuracy, robustness, and consistency with manual measurements. It shows strong potential for clinical applications, providing reliable technical support for eyelid morphological analysis and surgical planning.