Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps

• URL: http://arxiv.org/abs/2210.07102v1
• Date: Thu, 13 Oct 2022 15:34:20 GMT
• Title: Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps
• Authors: Juan S. Sierra, Jesus Pineda, Daniela Rueda, Alejandro Tello, Angelica M. Prada, Virgilio Galvis, Giovanni Volpe, Maria S. Millan, Lenny A. Romero, Andres G. Marrugo
• Abstract summary: This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
• Score: 48.498376125522114
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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