Limits...
Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection.

Xu J, Ishikawa H, Wollstein G, Bilonick RA, Folio LS, Nadler Z, Kagemann L, Schuman JS - PLoS ONE (2013)

Bottom Line: Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes.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.This new method has the potential to improve early detection of glaucomatous damage.

View Article: PubMed Central - PubMed

Affiliation: University of Pittsburgh Medical Center Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

ABSTRACT

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.

Show MeSH

Related in: MedlinePlus

An example of conventional circumpapillary retinal nerve fiber layer (cpRNFL) analysis as provided by Cirrus HD-OCT.(A) Overlay of RNFL thickness deviation map on the OCT fundus image with focal wedge defect (red arrows) predominantly outside the 3.4 mm diameter circle sampling (red circle). (B) Corresponding 2D RNFL thickness map, RNFL focal defect is marked with red arrows. (C) cpRNFL thickness profile along the 3.4 mm diameter circle is within the normal range (green range). Red arrow pointing to the approximate location of the RNFL wedge defect. (D) The RNFL thickness measurement is summarized in 4 quadrants and 12 clock hours with all sectoral measurements within the normal range.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3569462&req=5

pone-0055476-g001: An example of conventional circumpapillary retinal nerve fiber layer (cpRNFL) analysis as provided by Cirrus HD-OCT.(A) Overlay of RNFL thickness deviation map on the OCT fundus image with focal wedge defect (red arrows) predominantly outside the 3.4 mm diameter circle sampling (red circle). (B) Corresponding 2D RNFL thickness map, RNFL focal defect is marked with red arrows. (C) cpRNFL thickness profile along the 3.4 mm diameter circle is within the normal range (green range). Red arrow pointing to the approximate location of the RNFL wedge defect. (D) The RNFL thickness measurement is summarized in 4 quadrants and 12 clock hours with all sectoral measurements within the normal range.

Mentions: Spectral-domain (SD-) OCT’s fast scanning speed allows three-dimensional (3D) volume scanning of the retina, which may offer detailed and accurate quantitative analysis of the retinal structure. However, despite the rich information embedded in 3D OCT images, current standard quantitative structural OCT measurement is mostly limited to several hundred sampling points along a 3.4 mm circle diameter centered at optic nerve head, which does not take full advantage of the 3D dataset (over 20,000 sampling points). This sampling pattern was chosen mostly to allow compatibility with legacy data obtained using time-domain (TD-) OCT. The limited tissue sampling might lead to situations where early signs of structural changes are not detected when located outside the sampled circle (Fig. 1).


Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection.

Xu J, Ishikawa H, Wollstein G, Bilonick RA, Folio LS, Nadler Z, Kagemann L, Schuman JS - PLoS ONE (2013)

An example of conventional circumpapillary retinal nerve fiber layer (cpRNFL) analysis as provided by Cirrus HD-OCT.(A) Overlay of RNFL thickness deviation map on the OCT fundus image with focal wedge defect (red arrows) predominantly outside the 3.4 mm diameter circle sampling (red circle). (B) Corresponding 2D RNFL thickness map, RNFL focal defect is marked with red arrows. (C) cpRNFL thickness profile along the 3.4 mm diameter circle is within the normal range (green range). Red arrow pointing to the approximate location of the RNFL wedge defect. (D) The RNFL thickness measurement is summarized in 4 quadrants and 12 clock hours with all sectoral measurements within the normal range.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569462&req=5

pone-0055476-g001: An example of conventional circumpapillary retinal nerve fiber layer (cpRNFL) analysis as provided by Cirrus HD-OCT.(A) Overlay of RNFL thickness deviation map on the OCT fundus image with focal wedge defect (red arrows) predominantly outside the 3.4 mm diameter circle sampling (red circle). (B) Corresponding 2D RNFL thickness map, RNFL focal defect is marked with red arrows. (C) cpRNFL thickness profile along the 3.4 mm diameter circle is within the normal range (green range). Red arrow pointing to the approximate location of the RNFL wedge defect. (D) The RNFL thickness measurement is summarized in 4 quadrants and 12 clock hours with all sectoral measurements within the normal range.
Mentions: Spectral-domain (SD-) OCT’s fast scanning speed allows three-dimensional (3D) volume scanning of the retina, which may offer detailed and accurate quantitative analysis of the retinal structure. However, despite the rich information embedded in 3D OCT images, current standard quantitative structural OCT measurement is mostly limited to several hundred sampling points along a 3.4 mm circle diameter centered at optic nerve head, which does not take full advantage of the 3D dataset (over 20,000 sampling points). This sampling pattern was chosen mostly to allow compatibility with legacy data obtained using time-domain (TD-) OCT. The limited tissue sampling might lead to situations where early signs of structural changes are not detected when located outside the sampled circle (Fig. 1).

Bottom Line: Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes.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.This new method has the potential to improve early detection of glaucomatous damage.

View Article: PubMed Central - PubMed

Affiliation: University of Pittsburgh Medical Center Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus