Annotated spiral ganglion neuron training data for object detection

Tissue clearing and light-sheet fluorescence microscopy were applied for 3D profiling of intact cochleae. However, the spiral ganglion neurons (SGNs) remain relatively understudied compared to hair cells and supporting cells, especially in large animal models. Here we: 1) introduced collagenase treatment to the current protocol of tissue clearing to enhance uniform antibody staining of SGNs within the pig cochlea, and 2) adopted a deep learning object detection model to locate and count SGNs in large 3D datasets via Spiner (Spiral ganglion neuron profiler). This dataset contains the training, validation, and test data for the deep learning object detection model (RetinaNet) used in the Spiner workflow. Bounding boxes of SGNs are annotated in the dataset. , , # Annotated spiral ganglion neuron training data for object detection

Training, validation, and test data for RetinaNet model training, with annotated bounding boxes for spiral ganglion neurons in fluorescence images.

## Description of the data and file structure

This is the annotated data used for training the RetinaNet model in the [Spiner](https://doi.org/10.5281/zenodo.15273059) workflow. The trained RetinaNet model was used for the automated detection of spiral ganglion neurons in fluorescence images.

The images in this dataset were acquired using light sheet fluorescence microscopes, in which the spiral ganglion neurons (SGNs) were immunostained with PGP9.5 or TuJ1 antibody or in the autofluorescence channel obtained with illumination at 488nm.

The dataset contains three folders of images (for training, validation, and test respectively), bounding box annotations stored in three corresponding CSV files, and a \"classes_0.csv\" specifying the class label for model training. Deta...,