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

arXiv: https://arxiv.org/abs/2210.07102 · HTML: https://mlsanity.com/paper/2210.07102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images](https://mlsanity.com/paper/2011.03683/) (2020)
- [Quantitative Digital Microscopy with Deep Learning](https://mlsanity.com/paper/2010.08260/) (2020)
- [Extracting quantitative biological information from brightfield cell images using deep learning](https://mlsanity.com/paper/2012.12986/) (2020)
- [DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae](https://mlsanity.com/paper/2203.01882/) (2022)

## Abstract (arXiv)

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.