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Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Xu, J and Ishikawa, H and Wollstein, G and Bilonick, RA and Folio, LS and Nadler, Z and Kagemann, L and Schuman, JS (2013) Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection. PLoS ONE, 8 (2).

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Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Xu, J
Ishikawa, Hhii3@pitt.eduHII3
Wollstein, Gchw28@pitt.eduCHW28
Bilonick, RArab45@pitt.eduRAB45
Folio, LS
Nadler, Z
Kagemann, Llek19@pitt.eduLEK19
Schuman, JSjss28@pitt.eduJSS280000-0002-8885-3766
Date: 11 February 2013
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 8
Number: 2
DOI or Unique Handle: 10.1371/journal.pone.0055476
Schools and Programs: School of Medicine > Ophthalmology
Swanson School of Engineering > Bioengineering
Refereed: Yes
Other ID: NLM PMC3569462
PubMed Central ID: PMC3569462
PubMed ID: 23408988
Date Deposited: 20 Mar 2013 20:46
Last Modified: 02 Feb 2019 13:58


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