Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

• Published in: IEEE transactions on medical imaging Vol. 28; no. 9; pp. 1436 - 1447
• Main Authors: Garvin, M.K., Abramoff, M.D., Xiaodong Wu, Russell, S.R., Burns, T.L., Sonka, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analysis of Variance; Biomedical imaging; Biomedical optical imaging; Cities and towns; Cost function; Databases, Factual; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Information Storage and Retrieval; Macula Lutea - anatomy & histology; Oncology; Ophthalmology; optical coherence tomography; Optical sensors; Reproducibility of Results; retina; Retina - anatomy & histology; segmentation; spectral-domain; Studies; Testing; three-dimensional (3-D) graph search; Time domain analysis; Tomography; Tomography, Optical Coherence - methods
• DOI: 10.1109/TMI.2009.2016958
• ISSN: 0278-0062; 1558-254X

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of 5.69 plusmn 2.41 mum was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of 5.71 plusmn 1.98 mum.