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Use of varying constraints in optimal 3-D graph search for segmentation of macular optical coherence tomography images
Conference proceeding   Open access

Use of varying constraints in optimal 3-D graph search for segmentation of macular optical coherence tomography images

Mona Haeker, Michael D Abràmoff, Xiaodong Wu, Randy Kardon and Milan Sonka
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol.10(Pt 1), pp.244-251
2007
DOI: 10.1007/978-3-540-75757-3_30
PMID: 18051065
url
https://doi.org/10.1007/978-3-540-75757-3_30View
Published (Version of record) Open Access

Abstract

An optimal 3-D graph search approach designed for simultaneous multiple surface detection is extended to allow for varying smoothness and surface interaction constraints instead of the traditionally used constant constraints. We apply the method to the intraretinal layer segmentation of 24 3-D optical coherence tomography (OCT) images, learning the constraints from examples in a leave-one-subject-out fashion. Introducing the varying constraints decreased the mean unsigned border positioning errors (mean error of 7.3 +/- 3.7 microm using varying constraints compared to 8.3 +/- 4.9 microm using constant constraints and 8.2 +/- 3.5 microm for the inter-observer variability).
 Algorithms Macular Degeneration - etiology Reproducibility of Results Artificial Intelligence Humans Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Tomography, Optical Coherence - methods Optic Neuropathy, Ischemic - pathology Sensitivity and Specificity Optic Neuropathy, Ischemic - complications Image Enhancement - methods Ophthalmoscopy - methods Pattern Recognition, Automated - methods Macula Lutea - pathology Macular Degeneration - pathology

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