  To estimate the corneal endothelial parameters from specular microscopy
images depicting cornea guttata (Fuchs dystrophy), we propose a new deep
learning methodology that includes a novel attention mechanism named feedback
non-local attention (fNLA). Our approach first infers the cell edges, then
selects the cells that are well detected, and finally applies a postprocessing
method to correct mistakes and provide the binary segmentation from which the
corneal parameters are estimated (cell density [ECD], coefficient of variation
[CV], and hexagonality [HEX]). In this study, we analyzed 1203 images acquired
with a Topcon SP-1P microscope, 500 of which contained guttae. Manual
segmentation was performed in all images. We compared the results of different
networks (UNet, ResUNeXt, DenseUNets, UNet++) and found that DenseUNets with
fNLA provided the best performance, with a mean absolute error of 23.16
[cells/mm$^{2}$] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX, which was 3-6
times smaller than the error obtained by Topcon's built-in software. Our
approach handled the cells affected by guttae remarkably well, detecting cell
edges occluded by small guttae while discarding areas covered by large guttae.
Overall, the proposed method obtained accurate estimations in extremely
challenging specular images.
