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

By Mona Kathryn Garvin, Michael David Abràmoff, Xiaodong Wu, S. Russell, T. Burns, Milan Sonka. IEEE Transactions on Medical Imaging, 2009

667 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/80be1d2b1dfecbbaaeeed32a9d4cfa2e7f89e6c4

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery (2017, methodology): “We used an automatic layer segmentation algorithm following [15] to ﬁnd the top and bottom layer of the retina that deﬁne the border of the retina in z-direction.”
Retinal Imaging and Image Analysis (2010, neutral): “[128], flattening an image involved first segmenting the retinal pigment epithelial surface in a lower resolution, fitting a (regularized) thin-plate spline [140] to this surface, and then vertically realigning the columns of the volume to make this surface completely flat.”