A texture-aware U-Net for identifying incomplete blinking from eye videography

Accurate identification of incomplete blinking from eye videography is critical for the early detection of eye disorders or diseases (e.g., dry eye). In this study, we develop a texture-aware neural network based on the classical U-Net (termed TAU-Net) to accurately extract palpebral fissures from each frame of eye videography for assessing incomplete blinking. We introduced three different convolutional blocks based on element-wise subtraction operations to highlight subtle textures associated with target objects and integrated these blocks with the U-Net to improve the segmentation of palpebral fissures. Quantitative experiments on 1396 frame images showed that the developed network achieved an average Dice index of 0.9587 and a Hausdorff distance (HD) of 4.9462 pixels when applied to segment palpebral fissures. It outperformed the U-Net and its several variants, demonstrating a promising performance in identifying incomplete blinking based on eye videography.

Automated summary

This study develops a texture-aware neural network for accurately extracting palpebral fissures from eye videography. By introducing different convolutional blocks and integrating them with the U-Net, the network achieves promising performance in identifying incomplete blinking based on eye videography.