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- W4306311946 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." @default.
- W4306311946 created "2022-10-15" @default.
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- W4306311946 date "2022-10-13" @default.
- W4306311946 modified "2023-10-13" @default.
- W4306311946 title "Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps" @default.
- W4306311946 doi "https://doi.org/10.48550/arxiv.2210.07102" @default.
- W4306311946 hasPublicationYear "2022" @default.
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