Incorporation of regional information in optimal 3-D graph search with application for intraretinal layer segmentation of optical coherence tomography images

Authors:
Mona Haeker;Xiaodong Wu;Michael Abràmoff;Randy Kardon;Milan Sonka
Affiliations:
Departments of Electrical & Computer Engineering and Biomedical Engineering, The University of Iowa, Iowa City, IA;Departments of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA;Departments of Electrical & Computer Engineering and Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, IA;Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, IA;Departments of Electrical & Computer Engineering and Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, IA
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

We present a method for the incorporation of regional image information in a 3-D graph-theoretic approach for optimal multiple surface segmentation. By transforming the multiple surface segmentation task into finding a minimum-cost closed set in a vertex-weighted graph, the optimal set of feasible surfaces with respect to an objective function can be found. In the past, this family of graph search applications only used objective functions which incorporated "on-surface" costs. Here, novel "in-region" costs are incorporated. Our new approach is applied to the segmentation of seven intraretinal layer surfaces of 24 3-D macular optical coherence tomography images from 12 subjects. Compared to an expert-defined independent standard, unsigned border positioning errors are comparable to the inter-observer variability (7.8 ± 5.0 µm and 8.1 ± 3.6 µm, respectively).