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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 12 — Dec. 1, 2013
  • pp: 2712–2728

A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes

Bhavna J. Antony, Michael D. Abràmoff, Matthew M. Harper, Woojin Jeong, Elliott H. Sohn, Young H. Kwon, Randy Kardon, and Mona K. Garvin  »View Author Affiliations

Biomedical Optics Express, Vol. 4, Issue 12, pp. 2712-2728 (2013)

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Optical coherence tomography is routinely used clinically for the detection and management of ocular diseases as well as in research where the studies may involve animals. This routine use requires that the developed automated segmentation methods not only be accurate and reliable, but also be adaptable to meet new requirements. We have previously proposed the use of a graph-theoretic approach for the automated 3-D segmentation of multiple retinal surfaces in volumetric human SD-OCT scans. The method ensures the global optimality of the set of surfaces with respect to a cost function. Cost functions have thus far been typically designed by hand by domain experts. This difficult and time-consuming task significantly impacts the adaptability of these methods to new models. Here, we describe a framework for the automated machine-learning based design of the cost function utilized by this graph-theoretic method. The impact of the learned components on the final segmentation accuracy are statistically assessed in order to tailor the method to specific applications. This adaptability is demonstrated by utilizing the method to segment seven, ten and five retinal surfaces from SD-OCT scans obtained from humans, mice and canines, respectively. The overall unsigned border position errors observed when using the recommended configuration of the graph-theoretic method was 6.45 ± 1.87 μm, 3.35 ± 0.62 μm and 9.75 ± 3.18 μm for the human, mouse and canine set of images, respectively.

© 2013 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(100.6890) Image processing : Three-dimensional image processing
(110.4500) Imaging systems : Optical coherence tomography
(100.4994) Image processing : Pattern recognition, image transforms

ToC Category:
Image Processing

Original Manuscript: August 16, 2013
Revised Manuscript: October 24, 2013
Manuscript Accepted: October 27, 2013
Published: November 1, 2013

Bhavna J. Antony, Michael D. Abràmoff, Matthew M. Harper, Woojin Jeong, Elliott H. Sohn, Young H. Kwon, Randy Kardon, and Mona K. Garvin, "A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes," Biomed. Opt. Express 4, 2712-2728 (2013)

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