Deep learning-based segmentation and density estimation of corneal nerves and dendritic cells from In Vivo confocal microscopy images

Abstract

Abstract The purpose of this study was to compare manual assessment of corneal nerve fiber length (CNFL) and dendritic cell (DC) density with an automated assessment method utilizing deep learning segmentation to perform rule-based density estimation. Corneal images were acquired using in vivo confocal microscopy (IVCM) from 100 participants with persistent ocular symptoms after mild COVID-19 (Group 1) and 30 controls without symptoms (Group 2). In total, 1,300 IVCM images were selected and manually annotated for CNFL, and 1,300 for DCs (with dendrites and without dendrites), using FIJI tools. The between-method difference in mean CNFL density was 0.2 $$\hbox {mm/mm}^2$$ (95% CI: [0.09, 0.23]) for Group 1 and −0.2 $$\hbox {mm/mm}^2$$ (95% CI: [−0.34, −0.10]) for Group 2. For Group 1, the mean difference for DCs with dendrites was −1.1 $$\hbox {cells/mm}^2$$ (95% CI: [−1.78, −0.39]), and for DCs without dendrites it was −3.1 $$\hbox {cells/mm}^2$$ (95% CI: [−5.1, −1.0]). For Group 2, the mean difference for DCs with dendrites was −1.0 $$\hbox {cells/mm}^2$$ (95% CI: [−1.79, −0.27]), and for DCs without dendrites it was 0.3 $$\hbox {cells/mm}^2$$ (95% CI: [−1.93, 2.60]). Both manual and automated methods showed significant between-group differences for CNFL ( p =0.012 and p =0.034, respectively) and DC densities ( p =0.005 and p =0.010). The automated approach performed comparably to manual assessment, supporting its potential for reliable, scalable analysis of CNFL and DC in IVCM images.