Related Experiment Video
Updated: Dec 23, 2025
Author Spotlight: Advancing Corneal Innervation Research Through Innovative Models
Published on: December 8, 2023
Automatic recognition of corneal nerve structures in images from confocal microscopy
Fabio Scarpa1, Enrico Grisan, Alfredo Ruggeri
1Department of Information Engineering, University of Padova, Padova, Italy.
Purpose:
To devise a method for automatically tracing corneal nerves in confocal microscopy images.
Methods:
Images were acquired with a confocal microscope. They were normalized and enhanced in luminosity and contrast. The nerves were recognized by applying a novel tracing algorithm, which includes Gabor filtering to enhance nerve visibility and postprocessing procedures to remove false recognitions and to link sparse segments into continuous structures. A prototype of the algorithm was implemented in commercial software and run on a personal computer.
Results:
A retrospective evaluation of the automatic procedure was performed on a data set containing 90 images, from normal and non-normal subjects. The average percentage of correctly recognized nerves length with respect to total manually traced lengths of visible nerves was 80.4% in normal subjects and 83.8% on non-normal subjects; the average rate of false nerve length recognition (with respect to the total automatically traced length) was 6.5% in normal subjects and 9.1% in non-normal subjects. Correlation coefficients between manual and automatic lengths on the same image were 0.94, 0.95, and 0.86 in all, normal, and non-normal subjects, respectively. A further evaluation was performed on an independent set of 80 normal subject images, resulting in a correlation coefficient of 0.89 between manual and automatic nerve lengths.
Conclusions:
Automatic and manual length estimations on the same image were very well correlated, indicating that the automatic procedure is capable of correctly reproducing the differences in nerve length between different subjects.
