We have developed a new algorithm for ordering anterior chamber OCT images in such a way that it is possible to classify them, in a fully unsupervised manner, in meaningful groups according to relevant features. We have tested the algorithm with a large set of images classified by two expert ophthalmologists, and with a larger set of annotated images. We have verified that the separation in the different classes defined by the ophthalmologists (closed, narrow, open, and wide open_ figure 6) is similar when using the manually extracted features, or when using the features that are returned by the unsupervised algorithm (View figures).
Therefore, the abstract features generated by the algorithm provide novel tools for assessing OCT images of the anterior chamber. They can be used for direct classification of the images and, furthermore, they can be linked to established quantities used for characterizing diseased eyes (like chamber depth, iris-corneal angle) resulting in an automatic detection system. As the algorithm is fully unsupervised, it can be easily automated and set up in OCT imaging systems to aid technicians and doctors in an early diagnosis. The two main advantages of the algorithm demonstrated here over previous works are that it doesn’t need any ground truth or gold standard for training, and it does not rely on specific landmarks; thus, it can analyze images in which relevant landmarks are not visible or not easy to locate.
The outcome of the algorithm applied to the image database is presented in the
way of an “Image Map”. A regular grid in the coordinates space (w, v) is defined and one image per grid point is
displayed.
In Fig. 1 (IsoMap with Euclidean distance) it is apparent that the algorithm ordered the images according to the orientation (horizontal axis) and position (vertical axis) of each eye inside the OCT image, which are irrelevant
features, this simple algorithm is, then, not capable of extracting useful features. However, when including
the alignment step in the pre-processing of the images which removed the variability detected by the first algorithm,
meaningful features were extracted, as it can be seen in the Image Map in Fig. 2 (these features correlate
with the features derived manually,).
A similar map was obtained with the Hellinger distance shown in Fig. 3( see below) which marginally improves the performance with the Euclidean distance. To test the robustness to of the image ordering, the t-SNE algorithm was applied (instead of IsoMap).
The map obtained is shown in Fig. 4 (below) which turned out to perform slightly better than IsoMap.
In Fig. 5 the features returned by the unsupervised algorithm, using t-SNE, are compared with the features obtained from the manual annotation of the images: in the left panel the color code indicates the chamber depth, and in the right panel, it indicates the mean angle (average of α and β) of each annotated image. Clearly, the features obtained from the manual annotation correlate very well with the features returned by the unsupervised algorithm. However, one should notice that the annotated features are not independent, but strongly correlated with each other. Finally, the manual classification done by two expert ophthalmologists is included in the t-SNE (w, v) map.
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