Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders

Abstract

Purpose: To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment.

Methods: A novel bi-channel deep-learning variational autoencoder (biVAE) model was trained using 1498 optical coherence tomography (OCT) scans of 125 subjects over time from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and 791 OCT scans of 96 control subjects from the University of Iowa. An independent test dataset of 70 eyes from 70 papilledema subjects was used to evaluate the ability of the biVAE model to quantify and reconstruct the papilledema spatial patterns from input OCT scans using only two variables.

Results: The montage color maps of the retinal nerve fiber layer (RNFL) and total retinal thickness (TRT) produced by the biVAE model provided an organized visualization of the variety of morphological patterns of optic disc edema (including differing patterns at similar thickness levels). Treatment effects of acetazolamide versus placebo in the IIHTT were also demonstrated in the latent space. In image reconstruction, the mean signed peripapillary retinal nerve fiber layer thickness (pRNFLT) difference ± SD was -0.12 ± 17.34 µm, the absolute pRNFLT difference was 13.68 ± 10.65 µm, and the RNFL structural similarity index reached 0.91 ± 0.05.

Conclusions: A wide array of structural patterns of papilledema, integrating the magnitude of disc edema with underlying disc and retinal morphology, can be quantified by just two latent variables.

Translational relevance: A biVAE model encodes structural patterns, as well as the correlation between channels, and may be applied to visualize individuals or populations with papilledema throughout treatment.

Figure 1.

(A) Flowchart of the proposed biVAE model with example paired input RNFLT and TRT maps and the reconstructed outcome images. The color fundus photograph is provided as a reference. (B) The RNFL and TR latent space montage maps were created by the corresponding VAE decoder with latent variable pairs ranging from −29 to 4 (both the x-axis and y-axis). The corresponding decoder creates each tile in the latent space montage map according to the coordinates of latent variables: (d_R1, d_R2) = (4, 4), (3, 4), …, (−28, −29), (−29, −29) and (d_T1, d_T2) = (4, 4), (3, 4), …, (−28, −29), (−29, −29). The example reconstructed images in (A) are highlighted in both montage maps in (B).

Figure 2.

An example of retinal layer segmentation and the thickness maps of an ONH-centered OCT scan. (A) OCT B-scans and the segmented layer surfaces. (B, C) The corresponding RNFLT and TRT maps in three dimensions (using the same color scale). (D, E) The RNFLT and TRT maps in two dimensions (using adjusted color scales), which are the input for the proposed VAE model.

Figure 3.

Distribution of subject eyes in the RNFL latent space montage map, where the green dots represent the data from the Iowa normal subjects, and the red dots and blue dots represent the data from the IIHTT placebo and treatment groups, respectively. The upper-right inset, with the dashed outline, is a zoomed-in view of the montage map; the black contours are the KDE plots based on the Iowa normal data points. Three types of severe edema cases are displayed in the upper-left, lower-left, and lower-right panels. (A) In the subpanels, (a) is the color fundus photograph, (b) is the OCT central B-scan with automated layer segmentation, and (c) and (d) are the encoder's input and decoder's output RNFLT maps with the peripapillary circle, respectively. (B) pRNFLT montage maps that correspond to each tile of the original montage maps in (A). (C) The RNFL spatial patterns differ (i.e., latent values vary) even when the pRNFLTs are similar.

Figure 4.

IIHTT data distribution changes over time between the treatment (ACZ) and placebo groups. (A) KDE plots of data points in the RNFL latent space montage maps help visualize how the treatment group (blue contours) migrates to the upper-right corner close to the normal group (green contours) faster than the placebo group (red contours) does. (B) Box plots of latent variable d_R1 and d_R2 in the different treatment groups over time. The red stars indicate significant mean differences based on Tukey's HSD test (α = 0.05).

Figure 5.

IIHTT data (baseline and 6 months) relationships among the Frisén grades, pRNFLT measurements, and RNFL latent variable d_R1 and d_R2 pairs. (A) Data points with labels of Frisén grades superimposed on the RNFL latent space montage map, in which the color scale indicates the pRNFLT. (B) Data point trajectories (per eye) from the baseline to 6-month visits.

Figure 6.

A scatterplot showing a high correlation of pRNFLT measurements derived from the original and reconstructed thickness maps based on 70 independent subjects in the test dataset. Qualitative examples show that the VAE model can catch prominent, local spatial features in the input images and create closely reconstructed outputs.