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Volume Averaging of Spectral-Domain Optical Coherence Tomography Impacts Retinal Segmentation in Children

View Article: PubMed Central - PubMed

ABSTRACT

Purpose: To determine the influence of volume averaging on retinal layer thickness measures acquired with spectral-domain optical coherence tomography (SD-OCT) in children.

Methods: Macular SD-OCT images were acquired using three different volume settings (i.e., 1, 3, and 9 volumes) in children enrolled in a prospective OCT study. Total retinal thickness and five inner layers were measured around an Early Treatment Diabetic Retinopathy Scale (ETDRS) grid using beta version automated segmentation software for the Spectralis. The magnitude of manual segmentation required to correct the automated segmentation was classified as either minor (<12 lines adjusted), moderate (>12 and <25 lines adjusted), severe (>26 and <48 lines adjusted), or fail (>48 lines adjusted or could not adjust due to poor image quality). The frequency of each edit classification was assessed for each volume setting. Thickness, paired difference, and 95% limits of agreement of each anatomic quadrant were compared across volume density.

Results: Seventy-five subjects (median age 11.8 years, range 4.3–18.5 years) contributed 75 eyes. Less than 5% of the 9- and 3-volume scans required more than minor manual segmentation corrections, compared with 71% of 1-volume scans. The inner (3 mm) region demonstrated similar measures across all layers, regardless of volume number. The 1-volume scans demonstrated greater variability of the retinal nerve fiber layer (RNLF) thickness, compared with the other volumes in the outer (6 mm) region.

Conclusions: In children, volume averaging of SD-OCT acquisitions reduce retinal layer segmentation errors.

Translational relevance: This study highlights the importance of volume averaging when acquiring macula volumes intended for multilayer segmentation.

No MeSH data available.


Multilayer retinal segmentation. The RNFL is defined between the borders of the inner limiting membrane (ILM) and GCL; GCL between the RNFL and IPL; IPL between the GCL and INL; INL between the IPL and OPL; and OPL between the INL and ONL. BM, Bruch's membrane.
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i2164-2591-5-4-12-f02: Multilayer retinal segmentation. The RNFL is defined between the borders of the inner limiting membrane (ILM) and GCL; GCL between the RNFL and IPL; IPL between the GCL and INL; INL between the IPL and OPL; and OPL between the INL and ONL. BM, Bruch's membrane.

Mentions: Scans that qualified for analysis were processed using beta version segmentation software supplied by the manufacturer. This software uses a proprietary algorithm to automatically segment the different layers of the retina including the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner and outer plexiform layers (IPL/OPL), inner and outer nuclear layers (INL/ONL), and outer retinal layers including the RPE and Bruch's complex (Fig. 2). Each of the 61 b-scan frames of the volume were reviewed for segmentation errors by one reviewer (KV) who was blinded to all clinical information. If the layer was segmented incorrectly (Fig. 3), the reviewer manually adjusted the segmentation. After all segmentation errors were corrected, the individual layer thickness measures were calculated for the anatomic quadrants (superior, inferior, nasal, and temporal) of the inner (3 mm) and outer (6 mm) regions of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Quadrants with clipping artifact involving more than one b-scan were eliminated from the analysis.


Volume Averaging of Spectral-Domain Optical Coherence Tomography Impacts Retinal Segmentation in Children
Multilayer retinal segmentation. The RNFL is defined between the borders of the inner limiting membrane (ILM) and GCL; GCL between the RNFL and IPL; IPL between the GCL and INL; INL between the IPL and OPL; and OPL between the INL and ONL. BM, Bruch's membrane.
© Copyright Policy - cc-by-nc-nd
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4997888&req=5

i2164-2591-5-4-12-f02: Multilayer retinal segmentation. The RNFL is defined between the borders of the inner limiting membrane (ILM) and GCL; GCL between the RNFL and IPL; IPL between the GCL and INL; INL between the IPL and OPL; and OPL between the INL and ONL. BM, Bruch's membrane.
Mentions: Scans that qualified for analysis were processed using beta version segmentation software supplied by the manufacturer. This software uses a proprietary algorithm to automatically segment the different layers of the retina including the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner and outer plexiform layers (IPL/OPL), inner and outer nuclear layers (INL/ONL), and outer retinal layers including the RPE and Bruch's complex (Fig. 2). Each of the 61 b-scan frames of the volume were reviewed for segmentation errors by one reviewer (KV) who was blinded to all clinical information. If the layer was segmented incorrectly (Fig. 3), the reviewer manually adjusted the segmentation. After all segmentation errors were corrected, the individual layer thickness measures were calculated for the anatomic quadrants (superior, inferior, nasal, and temporal) of the inner (3 mm) and outer (6 mm) regions of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Quadrants with clipping artifact involving more than one b-scan were eliminated from the analysis.

View Article: PubMed Central - PubMed

ABSTRACT

Purpose: To determine the influence of volume averaging on retinal layer thickness measures acquired with spectral-domain optical coherence tomography (SD-OCT) in children.

Methods: Macular SD-OCT images were acquired using three different volume settings (i.e., 1, 3, and 9 volumes) in children enrolled in a prospective OCT study. Total retinal thickness and five inner layers were measured around an Early Treatment Diabetic Retinopathy Scale (ETDRS) grid using beta version automated segmentation software for the Spectralis. The magnitude of manual segmentation required to correct the automated segmentation was classified as either minor (<12 lines adjusted), moderate (>12 and <25 lines adjusted), severe (>26 and <48 lines adjusted), or fail (>48 lines adjusted or could not adjust due to poor image quality). The frequency of each edit classification was assessed for each volume setting. Thickness, paired difference, and 95% limits of agreement of each anatomic quadrant were compared across volume density.

Results: Seventy-five subjects (median age 11.8 years, range 4.3–18.5 years) contributed 75 eyes. Less than 5% of the 9- and 3-volume scans required more than minor manual segmentation corrections, compared with 71% of 1-volume scans. The inner (3 mm) region demonstrated similar measures across all layers, regardless of volume number. The 1-volume scans demonstrated greater variability of the retinal nerve fiber layer (RNLF) thickness, compared with the other volumes in the outer (6 mm) region.

Conclusions: In children, volume averaging of SD-OCT acquisitions reduce retinal layer segmentation errors.

Translational relevance: This study highlights the importance of volume averaging when acquiring macula volumes intended for multilayer segmentation.

No MeSH data available.