  Corneal thickness (pachymetry) maps can be used to monitor restoration of
corneal endothelial function, for example after Descemet's membrane endothelial
keratoplasty (DMEK). Automated delineation of the corneal interfaces in
anterior segment optical coherence tomography (AS-OCT) can be challenging for
corneas that are irregularly shaped due to pathology, or as a consequence of
surgery, leading to incorrect thickness measurements. In this research, deep
learning is used to automatically delineate the corneal interfaces and measure
corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three
different deep learning strategies were developed based on 960 B-scans from 50
patients. On an independent test set of 320 B-scans, corneal thickness could be
measured with an error of 13.98 to 15.50 micrometer for the central 9 mm range,
which is less than 3% of the average corneal thickness. The accurate thickness
measurements were used to construct detailed pachymetry maps. Moreover,
follow-up scans could be registered based on anatomical landmarks to obtain
differential pachymetry maps. These maps may enable a more comprehensive
understanding of the restoration of the endothelial function after DMEK, where
thickness often varies throughout different regions of the cornea, and
subsequently contribute to a standardized postoperative regime.
