Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT

Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT
Title:
Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT
Other Titles:
IEEE Transactions on Medical Imaging
DOI:
10.1109/TMI.2017.2703147
Keywords:
Publication Date:
01 September 2017
Citation:
H. Fu et al., "Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT," in IEEE Transactions on Medical Imaging, vol. 36, no. 9, pp. 1930-1938, Sept. 2017. doi: 10.1109/TMI.2017.2703147
Abstract:
Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through Anterior Segment Optical Coherence Tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar dataset, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus HD-OCT datasets demonstrate the effectiveness of our approach.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0278-0062
1558-254X
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