DOI: 10.5281/zenodo.17544799
title: Ex vivo multispecies dataset for three-dimensional reconstruction of the corneal stromal nerve plexus using hexapod-based confocal microscopy and deep-learning segmentation

description: This dataset provides three-dimensional confocal microscopy images and deep-learning-based nerve segmentations of the stromal corneal nerve plexus from ex vivo samples of multiple species (porcine, murine, ovine, and human). Corneal volumes were acquired using a confocal microscope synchronized with a programmable six-axis hexapod platform that precisely positioned the specimens, enabling large-scale volume scans of the corneal stroma. A U-Net-based 2.5D segmentation model was trained exclusively on one porcine cornea, achieving an Intersection-over-Union (IoU) of 0.92. The same model was subsequently applied to additional porcine, murine, ovine, and human corneas without retraining. The dataset includes large-area en face mosaics reconstructed from volume stitching for each depth level, along with corresponding automated nerve segmentations. Additionally, the manually annotated nerves used as ground truth for model training are provided.
description: Confocal image acquisition was performed using a Heidelberg Retina Tomograph II and Rostock Cornea Module (both Heidelberg Engineering, Germany). The microscope was mounted above the programmable six-axis Hexapod H-840 G12 (Physik Instrumente, Germany), which precisely positioned the ex vivo corneal samples. Synchronization between the microscope and the hexapod was achieved via a custom-built programmable electronic foot switch that triggered both the image acquisition and axial scanning movement of the hexapod.

Volumetric imaging was carried out by sequentially recording microscopy images while translating the sample along the axial direction to generate a volume scan. After each volume scan, the hexapod translated the sample laterally along a grid in a spiral pattern, allowing for continuous coverage of large stromal regions.An image processing workflow comprising noise reduction, dark-field correction, image registration, and volume stitching produced continuous three-dimensional datasets. The resulting en face mosaics of individual depth levels were used as input for the nerve segmentation pipeline.

Automated nerve segmentation was performed using a U-Net-based 2.5D deep learning model trained with supervised learning. Here, the term "2.5D" refers to the model's use of volumetric context during prediction: iteratively, nine consecutive image slices (the target slice plus four above and four below) were provided as input, while only the central slice was segmented as output. This approach allowed the network to incorporate spatial information from adjacent layers, improving its ability to distinguish nerve fibers from noise or other stromal structures. The model was trained on one manually segmented porcine cornea. The trained model was subsequently applied to datasets from six porcine, one murine, one ovine, and one human corneas without additional retraining.

For further methodological details, please refer to the related research article.
description: Ex vivo specimens:



Six porcine eyes (8 h after enucleation)

One murine eye (5 h after enucleation)

One ovine eye (5 h after enucleation)

One human eye (4 h after enucleation and ~24 h post mortem)


Volume reconstruction:



Sequential volume scans followed by lateral translation along a grid in a spiral pattern

Up to 8 x 8 individual volume scans

FOV of individual volume scan: 300 µm x 300 µm (384 x 384 pixel)

Frame rate: 30 Hz

Axial scan speed of hexapod: 30 µm/s

Resulting voxel size (X x Y x Z): 0.78125 µm x 0.78125 µm x 1 µm

Lateral overlap of individual volume scans: 50 µm

Stitched stromal volume size: up to 2.05 mm x 2.05 mm x 0.6 mm


Data format:



Reconstructed volumes of the corneal stroma are provided as image sequences

Each image represents an en face slice at 1 µm axial spacing

Corresponding nerve segmentations for every slice

Ground-truth annotation is provided for one porcine cornea

Image format: 8-bit grayscale TIFF (ZIP compressed)


Please note:The nerve segmentation contains artifacts caused by registration gaps at high-contrast edges. Manual removal of these artifacts is recommended before performing quantitative analyses.

The datasets PorcineCornea_00-Cropped and PorcineCornea_00-Full are from the same cornea. However, since cropping was performed on the individual volume scans before image registration, the resulting reconstructed volumes differ slightly.