The optic nerve head is the location in the rear of the eye where the nerves exit the eye towards the brain. Swelling of the optic nerve head (ONH) is most accurately quantitatively assessed via volumetric measures using 3D spectral-domain optical coherence tomography (SD-OCT). However, SD-OCT is not always available as its use is primarily limited to specialized eye clinics rather than in primary care or telemedical settings. Thus, there is still a need for severity assessment using more widely available 2D fundus photographs.
In this work, we propose machine-learning methods to locally estimate the volumetric measurements (akin to those produced by 3D SD-OCT images) of the optic disc swelling at each pixel location from only a 2D fundus photograph as the input. For training purposes, a thickness map of the swelling (reflecting the distance between the top and bottom surfaces of the ONH and surrounding retina) as measured from SD-OCT at each pixel location was used as the ground truth. First, a random-forest classifier was trained to output each thickness value from local fundus features pertaining to textural and color information. Eighty-eight image pairs of ONH-centered SD-OCT and registered fundus photographs from different subjects with optic disc swelling were used for training and evaluating the model in a leave-one-subject-out fashion.
Comparing the thickness map from the proposed method to the ground truth via SD-OCT, a root-mean-square (RMS) error of 1.66 mm³ for the entire ONH region was achieved, and Spearman's correlation coefficient was R= 0.73. Regional volumes for the nasal, temporal, inferior, superior, and peripapillary regions had RMS errors of 0.64 mm³, 0.61 mm³, 0.74 mm³, 0.71 mm³, and 1.30 mm³, respectively, suggesting that there is enough evidence in a singular color fundus photograph to estimate local swelling information.
Because of the recent success of deep-learning methods in imaging domains, a convolutional neural network was also trained using the same data as was used with the random forest classifier. Because training data is used to help fine tune model parameters for deep learning, a subset of twelve randomly selected patients was strictly withheld from the training process to be used for testing. Comparing the prediction results on the withheld data with the OCT ground truth, we achieved a root-mean-square (RMS) error of 2.07 mm³ for the entire ONH region. Regional volumes for the nasal, temporal, inferior, superior, and peripapillary regions had RMS errors of 0.75 mm³, 0.82 mm³, 0.85 mm³, 0.91 mm³, and 1.62 mm³, respectively. Although the errors are slightly higher than those from the random forest model, the test dataset was smaller as we could not use a leave-patient-out validation approach and this may not be representative of the whole dataset since results were not averaged as before. It is also known that deep learning models require larger training datasets to achieve similar results to traditional machine-learning methods. For these reasons, and the fact that the errors were close to those of traditional methods, we believe deep learning approaches for estimating local retinal thickness in cases of optic disc swelling still holds promise with larger datasets.
Both of the proposed approaches allow for clinicians to assess optic nerve edema in both a qualitative and quantitative manner using strictly fundus photography. The predictions allow for overall optic nerve head volume to be calculated as well as regional and local volumes which was not possible before.