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Macular hole morphology and measurement using an automated three-dimensional image segmentation algorithm
OBJECTIVE: Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coherence tomography (OCT) images of 104 MH of patients...
Tallennettuna:
| Julkaisussa: | BMJ Open Ophthalmol |
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| Päätekijät: | , , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
BMJ Publishing Group
2020
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7430427/ https://ncbi.nlm.nih.gov/pubmed/32844119 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1136/bmjophth-2019-000404 |
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