Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps
This provides a promising alternative for automated corneal endothelium assessment in medical imaging, particularly for patients with Fuchs' dystrophy, though it appears incremental as it builds on existing UNet architectures.
The paper tackled the challenge of assessing corneal endothelium in specular microscopy images with Fuchs' dystrophy by proposing a UNet-based segmentation method that regresses signed distance maps, achieving reliable morphometric assessment and guttae identification with an average cell density difference of -41.9 cells/mm² and mean cell area difference of 14.8 µm² compared to manual ground-truth.
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.