Model-driven 3-D regularisation for robust segmentation of the refractive corneal surfaces in spiral OCT scans
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 10554 LNCS, Page: 109-117
2017
- 3Citations
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85029796834&origin=inward; http://dx.doi.org/10.1007/978-3-319-67561-9_12; https://link.springer.com/10.1007/978-3-319-67561-9_12; https://dx.doi.org/10.1007/978-3-319-67561-9_12; https://link.springer.com/chapter/10.1007/978-3-319-67561-9_12
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know