  Specular microscopy assessment of the human corneal endothelium (CE) in
Fuchs' dystrophy is challenging due to the presence of dark image regions
called guttae. This paper proposes a UNet-based segmentation approach that
requires minimal post-processing and achieves reliable CE morphometric
assessment and guttae identification across all degrees of Fuchs' dystrophy. We
cast the segmentation problem as a regression task of the cell and gutta signed
distance maps instead of a pixel-level classification task as typically done
with UNets. Compared to the conventional UNet classification approach, the
distance-map regression approach converges faster in clinically relevant
parameters. It also produces morphometric parameters that agree with the
manually-segmented ground-truth data, namely the average cell density
difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5])
and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9,
71.5]). These results suggest a promising alternative for CE assessment.
