ArticleDiagnostics (Basel, Switzerland)2024
Segmentation-Based Measurement of Orbital Structures: Achievements in Eyeball Volume Estimation and Barriers in Optic Nerve Analysis.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Automated Semisupervised Measurement of Optic Nerve Sheath Diameter From CT Following Traumatic Brain Injury.International journal of biomedical imaging · 2026Article
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6 authors.
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Abstract
BACKGROUND/
objectiveOrbital diseases often require precise measurements of eyeball volume, optic nerve sheath diameter (ONSD), and apex-to-eyeball distance (AED) for accurate diagnosis and treatment planning. This study aims to automate and optimize these measurements using advanced deep learning segmentation techniques on orbital Computed Tomography (CT) scans.
methodsOrbital CT datasets from individuals of various age groups and genders were used, with annotated masks for the eyeball and optic nerve. A 2D attention U-Net architecture was employed for segmentation, enhanced with slice-level information embeddings to improve contextual understanding. After segmentation, the relevant metrics were calculated from the segmented structures and evaluated for clinical applicability.
resultsThe segmentation model demonstrated varying performance across orbital structures, achieving a Dice score of 0.8466 for the eyeball and 0.6387 for the optic nerve. Consequently, eyeball-related metrics, such as eyeball volume, exhibited high accuracy, with a root mean square error (RMSE) of 1.28-1.90 cm
conclusionsDespite these challenges, the study highlights the potential of deep learning-based segmentation to automate the assessment of ocular structures, particularly in measuring eyeball volume, while leaving room for further improvement in optic nerve analysis.
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