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An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization.

Qincheng Qiao^1,2, Juan Cao³, Xinguo Hou^4,5,6,7

¹Department of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.

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|May 8, 2026

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This study introduces a new, expert-annotated dataset of corneal images for nerve segmentation research. The dataset aims to improve the development and validation of algorithms for analyzing the sub-basal nerve plexus.

Area of Science:

Ophthalmology
Neuroscience
Medical Imaging

Background:

Corneal confocal microscopy visualizes the sub-basal nerve plexus in vivo.
Existing datasets often lack pixel-level annotations, hindering segmentation research.
Current resources have variable quality and annotation strategies, complicating algorithm comparison.

Purpose of the Study:

Introduce a novel, high-quality dataset for corneal nerve segmentation.
Provide expert-reviewed pixel-level nerve masks for 410 images.
Facilitate training, validation, and generalisability assessment of segmentation models.

Main Methods:

Collected 410 high-quality corneal images from 88 participants.
Generated expert-reviewed pixel-level nerve masks for all images.

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Included demographic, clinical, and laboratory data for participants.

Main Results:

Developed a dataset with detailed, manually curated nerve delineations.
Ensured consistent formatting and clear provenance for all data.
Dataset spans two subsets with distinct acquisition conditions.

Conclusions:

The new dataset addresses limitations in existing resources for corneal nerve segmentation.
It supports robust model development and generalisability testing.
Enables exploration of associations between image metrics and clinical characteristics.