ArticleBiomedical optics express2018
Active contour method for ILM segmentation in ONH volume scans in retinal OCT.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- [MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].Die Ophthalmologie · 2025Review
- Annotation-efficient learning for OCT segmentation.Biomedical optics express · 2023Article
- Deep learning network with differentiable dynamic programming for retina OCT surface segmentation.Biomedical optics express · 2023Article
- Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes.Sensors (Basel, Switzerland) · 2019Article
- Automatic identification and characterization of the epiretinal membrane in OCT images.Biomedical optics express · 2019Article
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5 authors.
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Abstract
The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then rescaled locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution.
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