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Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography
Journal article   Open access

Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography

Hrvoje Bogunovic, Milan Sonka, Young H Kwon, Pavlina Kemp, Michael D Abramoff and Xiaodong Wu
IEEE Transactions on Medical Imaging, Vol.33(12), pp.2242-2253
12/2014
DOI: 10.1109/TMI.2014.2336246
PMCID: PMC4326334
PMID: 25020067
url
http://doi.org/10.1109/TMI.2014.2336246View
Open Access

Abstract

When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graph-theoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2-D en-face alignment, all the fields are segmented simultaneously, imposing a priori soft interfield-intrasurface constraints for each pair of overlapping fields. The constraints penalize deviations from the expected surface height differences, taken to be the depth-axis shifts that produce the maximum cross-correlation of pairwise-overlapped areas. The method's accuracy and reproducibility are evaluated qualitatively and quantitatively on 212 OCT images (20 nine-field, 32 single-field acquisitions) from 26 patients with glaucoma. Qualitatively, the obtained thickness maps show no stitching artifacts, compared to pronounced stitches when the fields are segmented independently. Quantitatively, two ophthalmologists manually traced four intraretinal layers on 10 patients, and the average error ( 4.58 ±1.46 μm) was comparable to the average difference between the observers ( 5.86±1.72 μm). Furthermore, we show the benefit of the proposed approach in co-segmenting longitudinal scans. As opposed to segmenting layers in each of the fields independently, the proposed co-segmentation method obtains consistent segmentations across the overlapped areas, producing accurate, reproducible, and artifact-free results.
Ophthalmology image co-segmentation Image segmentation Three-dimensional displays mosaicing Retina Robustness Graph theory retinal layer segmentation

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