Fu, H., Xu, Y., Lin, S. et al. (6 more authors) (2017) Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT. IEEE Transactions on Medical Imaging , 36 (9). pp. 1930-1938. ISSN 0278-0062
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 datadriven 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. |
Keywords: | Data-driven; segmentation; AS-OCT; anterior chamber angle; angle-closure glaucoma |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jun 2017 10:33 |
Last Modified: | 03 Aug 2023 12:31 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TMI.2017.2703147 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117804 |