Abstract
Purpose :
Patients suffering from retinal diseases affecting central vision often undergo OCT and microperimetry to assess both retinal structure and function. However, it remains challenging for clinicians to merge this data to perform structure-function analysis. To address this, we investigated automated structure-function analysis of OCT and microperimetry data.
Methods :
We acquired OCT image data (Spectralis, Heidelberg) and microperimetry data (MAIA, Centervue) from 10 eyes of 6 healthy volunteers. Each OCT scan consisted of 145 B-mode slices and fundus image. Each MAIA scan consisted of 37 macular sensitivity scores and a fundus image. We segmented blood vessels in the fundus images using a multi-scale vessel filter. These were converted into intensity features through an inverse distance transform. The MAIA fundus images were transformed to the OCT coordinate frame through affine intensity-based registration, using the normalized cross-correlation between the distance-transformed segmented vessels. The retinal nerve fibre layer (RNFL) was automatically segmented using a graph-based algorithm. The central locations of the MAIA macular sensitivity scores were automatically extracted using a circular Hough transform and registered to the OCT domain. Finally, we extracted RNFL thicknesses at the locations of the sensitivity scores using cubic interpolation.
Results :
We measured the test-retest reliability of determining the RNFL thicknesses at the macular sensitivity locations using our proposed method. We found a Pearson’s correlation coefficient of 0.88, and an intraclass correlation coefficient of 0.88 (95% confidence interval 0.86-0.90) using a two-way mixed effects model for absolute agreement.
Conclusions :
Here we report that automated structure-function analysis of OCT and microperimetry data is possible with high repeatability in healthy retinas. Therefore, this method can enable systematic and longitudinal structure-function analysis of retinal diseases affecting central vision.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.