ArticleBiomedical signal processing and control2022
A texture-aware U-Net for identifying incomplete blinking from eye videography.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Uncertainty- and hardness-weighted loss functions for medical image segmentation.Engineering applications of artificial intelligence · 2026Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- PE-MT: A Perturbation-Enhanced Mean Teacher for Semi-Supervised Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.Frontiers in physiology · 2024Article
- Automated segmentation of five different body tissues on computed tomography using deep learning.Medical physics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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Registered trials
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