Sierra, Juan S.Pineda, JesusRueda, DanielaTello, AlejandroPrada, Angélica M.Galvis, VirgilioVolpe, GiovanniMillan, Maria S.Romero, Lenny A.Marrugo, Andres G.2023-07-212023-07-2120232023Sierra, J. S., Pineda, J., Rueda, D., Tello, A., Prada, A. M., Galvis, V., ... & Marrugo, A. G. (2023). Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps. Biomedical optics express, 14(1), 335-351.https://hdl.handle.net/20.500.12585/12342Specular 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 µm2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.17 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/article10.1364/BOE.477495Corneal Endothelium;Hexagonal Cells;Capillary Endothelial Cellinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarLEMB