ArticlePattern recognition2021
Automated delineation of corneal layers on OCT images using a boundary-guided CNN.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 26 citations in OpenAlex.
- Uncertainty- and hardness-weighted loss functions for medical image segmentation.Engineering applications of artificial intelligence · 2026Article
- Leveraging CNN and Transfer Learning With EfficientNet for Enhanced Optical Coherence Tomography (OCT).International journal of biomedical imaging · 2026Article
- Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.Translational vision science & technology · 2025Article
- Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography.NPJ digital medicine · 2024Article
- A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.Sensors (Basel, Switzerland) · 2024Review
- ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model.Heliyon · 2024Article
- UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.Frontiers in physiology · 2024Article
- Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.Medical image analysis · 2023Article
- Predicting left/right lung volumes, thoracic cavity volume, and heart volume from subject demographics to improve lung transplant.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automated segmentation of five different body tissues on computed tomography using deep learning.Medical physics · 2023Article
- A texture-aware U-Net for identifying incomplete blinking from eye videography.Biomedical signal processing and control · 2022Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
Accurate segmentation of corneal layers depicted on optical coherence tomography (OCT) images is very helpful for quantitatively assessing and diagnosing corneal diseases (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.