ArticleFrontiers in medicine2023
Measurement method of tear meniscus height based on deep learning.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 19 citations in OpenAlex.
- Associations between quantitative ocular surface features and CT morphology of the levator palpebrae superioris complex in patients with thyroid eye disease and upper eyelid retraction: a cross-sectional study.Frontiers in medicine · 2026Article
- Multicentre Pixel-Level Tear Meniscus Segmentation Dataset with Multimodal Imaging for Dry Eye Diagnosis.Scientific data · 2025Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- STB-Net: a Siamese architecture-based reconstruction-segmentation network for ocular surface image segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence-assisted tear meniscus height measurement: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control.Frontiers in bioengineering and biotechnology · 2025Article
- Evaluating the Effect of Image Enhancement on Diagnostic Reliability in Dry Eye Disease Using a Portable Imaging Device.Diagnostics (Basel, Switzerland) · 2024Article
- Feasibility of Tear Meniscus Height Measurements Obtained with a Smartphone-Attachable Portable Device and Agreement of the Results with Standard Slit Lamp Examination.Diagnostics (Basel, Switzerland) · 2024Article
- A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence.Investigative ophthalmology & visual science · 2023Article
- Dynamic tear meniscus parameters in complete blinking: insights into dry eye assessment.International journal of ophthalmology · 2023Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
- Tear meniscus height comparison between AS-OCT and Oculus Keratograph® K5M.Romanian journal of ophthalmologyArticle
- Next-Generation Tear Meniscus Height Detecting and Measuring Smartphone-Based Deep Learning Algorithm Leads in Dry Eye Management.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tear meniscus height (TMH) is an important reference parameter in the diagnosis of dry eye disease. However, most traditional methods of measuring TMH are manual or semi-automatic, which causes the measurement of TMH to be prone to the influence of subjective factors, time consuming, and laborious. To solve these problems, a segmentation algorithm based on deep learning and image processing was proposed to realize the automatic measurement of TMH. To accurately segment the tear meniscus region, the segmentation algorithm designed in this study is based on the DeepLabv3 architecture and combines the partial structure of the ResNet50, GoogleNet, and FCN networks for further improvements. A total of 305 ocular surface images were used in this study, which were divided into training and testing sets. The training set was used to train the network model, and the testing set was used to evaluate the model performance. In the experiment, for tear meniscus segmentation, the average intersection over union was 0.896, the dice coefficient was 0.884, and the sensitivity was 0.877. For the central ring of corneal projection ring segmentation, the average intersection over union was 0.932, the dice coefficient was 0.926, and the sensitivity was 0.947. According to the evaluation index comparison, the segmentation model used in this study was superior to the existing model. Finally, the measurement outcome of TMH of the testing set using the proposed method was compared with manual measurement results. All measurement results were directly compared via linear regression; the regression line was y0.98x-0.02, and the overall correlation coefficient was
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