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

• Published in: IEEE transactions on medical imaging Vol. 32; no. 3; pp. 531 - 543
• Main Authors: Dufour, P. A., Ceklic, L., Abdillahi, H., Schroder, S., De Dzanet, S., Wolf-Schnurrbusch, U., Kowal, J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Computational modeling; Databases, Factual; Diagnostic Techniques, Ophthalmological; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Macular Degeneration - pathology; Models, Biological; Models, Statistical; Ophthalmology; optical coherence tomography (OCT); optimal net surface problems; Pathology; Retina; Retina - anatomy & histology; Retina - pathology; Retinal Drusen - pathology; segmentation; Silicon; Studies; Tomography, Optical Coherence - methods; Training
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2012.2225152

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.