ArticleDiagnostics (Basel, Switzerland)2022
Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Application of artificial intelligence in differentiating IgG4-related ophthalmic disease and orbital MALT lymphoma: a review of radiomics and deep learning advances.Frontiers in immunology · 2026Review
- Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images.International journal of ophthalmology · 2025Article
- Deep-Learning-Based Segmentation of Extraocular Muscles from Magnetic Resonance Images.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm
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