Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral {OCT} Scans

Wagner, Joerg and Pezold, Simon and Cattin, Philippe C. . (2017) Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral {OCT} Scans. In: Fetal, Infant and Ophthalmic Medical Image Analysis . Cham, pp. 109-117.

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Official URL: https://edoc.unibas.ch/63983/

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Measuring the cornea’s anterior and posterior refractive surface is essential for corneal topography, used for diagnostics and the planning of surgeries. Corneal topography by Optical Coherence Tomography (OCT) relies on proper segmentation. Common segmentation methods are limited to specific, B-scan-based scan patterns and fail when applied to data acquired by recently proposed spiral scan trajectories. We propose a novel method for the segmentation of the anterior and posterior refractive surface in scans acquired by 2-D scan trajectories – including but not limited to spirals. Key feature is a model-driven, three-dimensional regularisation of the region of interest, slope and curvature. The regularisation is integrated into a graph-based segmentation with feature-directed smoothing and incremental segmentation. We parameterise the segmentation based on test surface measurements and evaluate its performance by means of 18 in vivo measurements acquired by spiral and radial scanning. The comparison with expert segmentations shows successful segmentation of the refractive corneal surfaces.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Center for medical Image Analysis & Navigation (Cattin)
UniBasel Contributors:Cattin, Philippe Claude
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name:Lecture Notes in Computer Science book series (LNCS)
Issue Number:10554
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:29 May 2018 09:30
Deposited On:29 May 2018 09:30

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