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

Corneal confocal microscopy enables in vivo visualisation of the sub-basal nerve plexus; however, most available datasets provide only centreline annotations, and open pixel-level annotations are comparatively scarce, limiting broader pixel-level segmentation research. Existing resources vary in quality, annotation strategy, and clinical coverage, complicating objective algorithm comparisons and reducing their utility in developing robust models. To address these gaps, we introduce a new dataset of 410 high-quality images from 88 participants, each paired with an expert-reviewed pixel-level nerve mask. These images span two independently collected subsets with distinct acquisition conditions and participant groups. Demographic information is available for each participant, and one subset contained detailed clinical and laboratory data. The dataset offers detailed, manually curated delineations of nerve fibres, consistent formatting, and clear provenance. It is intended to support the training and validation of segmentation models, assess generalisability across imaging conditions, and explore the associations between image-derived metrics and clinical characteristics.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-05-08
DOI
https://doi.org/10.1038/s41597-026-07418-6
Primary Topic
Ocular Surface and Contact Lens
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Juan Cao, Xinguo Hou, Qincheng Qiao
Scientific Data
Ocular Surface and Contact Lens
article

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

Juan Cao, Xinguo Hou, Qincheng Qiao
article en

Abstract

Corneal confocal microscopy enables in vivo visualisation of the sub-basal nerve plexus; however, most available datasets provide only centreline annotations, and open pixel-level annotations are comparatively scarce, limiting broader pixel-level segmentation research. Existing resources vary in quality, annotation strategy, and clinical coverage, complicating objective algorithm comparisons and reducing their utility in developing robust models. To address these gaps, we introduce a new dataset of 410 high-quality images from 88 participants, each paired with an expert-reviewed pixel-level nerve mask. These images span two independently collected subsets with distinct acquisition conditions and participant groups. Demographic information is available for each participant, and one subset contained detailed clinical and laboratory data. The dataset offers detailed, manually curated delineations of nerve fibres, consistent formatting, and clear provenance. It is intended to support the training and validation of segmentation models, assess generalisability across imaging conditions, and explore the associations between image-derived metrics and clinical characteristics.

Scientific Data
Shandong University (CN), Qilu Hospital of Shandong University (CN), Shandong Center for Disease Control and Prevention (CN)
National Key Research and Development Program of China
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Ocular Surface and Contact Lens
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.