Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Dufour, Pascal A; Ceklic, Lala; Abdillahi, Hannan; Schroder, Simon; De Dzanet, Sandro; Wolf-Schnurrbusch, Ute; Kowal, Jens (2013). Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints. IEEE transactions on medical imaging, 32(3), pp. 531-43. New York, N.Y.: Institute of Electrical and Electronics Engineers IEEE 10.1109/TMI.2012.2225152

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Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Item Type:

Journal Article (Original Article)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology

UniBE Contributor:

Dufour, Pascal André; Wolf-Schnurrbusch, Ute and Kowal, Horst Jens


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health




Institute of Electrical and Electronics Engineers IEEE




Factscience Import

Date Deposited:

04 Oct 2013 14:34

Last Modified:

05 Dec 2022 14:10

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URI: (FactScience: 220297)

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