HiNeuron is a challenging neuronal image dataset with different levels of densities, in which each level of data has hundreds or thousands of image blocks. We extract the challenging data of tangled filaments from the fluorescence micro-optical sectioning tomography (fMOST). These data mainly include tangled filament features such as neighboring fibers, branching and crossover structures, and dense fibers.

In the dataset, all the images are divided into three different density levels, ranging from low to high density. The dataset comprises approximately 2,800 image blocks and consists of neuronal images and their corresponding manual tracing result as the gold standard, and isotropic image through image enhancement techniques. The image voxel resolution is 0.35 µm × 0.35 µm × 1 µm or 0.32 µm × 0.32 µm × 1 µm, and the data are 3D images saved in 16-bit depth LZW-compressed TIFF format.

The Hineuron dataset can be used to analyze the reconstruction performance of existing neuron reconstruction algorithms and also contribute to the development of new neuron reconstruction algorithms. We make the dataset publicly available and provide the data download links, thereby offering a foundational data resource for future research on neuronal reconstruction. The dataset is available for free to researchers for non-commercial use.

HiNeuron dataset with different levels of densities

For questions and additional information, or would like to give us any suggestions, please contact us.

If you would like to use the dataset for commercial purposes, please contact us.
The email address: aali@hust.edu.cn.

Related publication:
Chen, W., Liao, M., Bao, S., An, S., Li, W., Liu, X., Huang, G., Gong, H., Luo, Q., Xiao, C., and Li, A. (2024). A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction. Patterns, 101007.

The HiNeuron dataset, which comprises neuronal images, corresponding gold standard results, and isotropic high-resolution voxel images for each neuronal image block. All the images are 3D in 16-bit format. The HiNeruon dataset is from different specimens, with different neuron data under each folder and each neuron data contains different levels of density. In each level, three folders store the original neuron images, corresponding isotropic images, and reconstructed gold standard results. At the same time, two txt files are provided, providing the image numbers in the dataset, and the direction provides the starting point and direction of the gold standard to be reconstructed in each neuron image block.

HiNeuron dataset information

HiNeuron dataset comes from two fMOST imaging systems, and there may be differences in image features. Currently, these two types of data are mainly used for single neuron reconstruction:
TDI-fMOST: The image blocks are 3D tiff, and the voxel resolution is 0.35 µm × 0.35 µm × 1 µm. There are 22 data files, totaling 1344 image blocks, and each file is 0.08 GB ~ 2.6 GB.
HD-fMOST: The image blocks are 3D tiff, and the voxel resolution is 0.32 µm × 0.32 µm × 1 µm. There are 17 data files, totaling 1465 image blocks, each file is 0.03 GB ~ 1.8 GB.

In each neuron data folder, there are different levels of data, and in each level of the folder, there will be three folders and two txt files, as follows:
image: the original neuron image block.
image_isotopic: the isotropic image corresponding to each original neuron image block.
swc: the target neurons reconstructed result in each image block.
direction.txt: the initial point and direction of the reconstructed target neuron.
id.txt: the number of all images under this folder.

The HiNeuron dataset can be opened through ImageJ, GTree, and Amira. ImageJ is a public domain software for processing and analyzing scientific images. GTree and Amira can load SWC files that overlap with the target fibers in the neuron image.

The specimen information

Download
Here is a sample download for the HiNeuron dataset, taking Neuron1 from Specimen1 as an example. Neuron1 includes three folders for each level, and within each level, there are three folders and two txt files. Each level represents a different density of neuronal image. In Level 1, the neuronal image density is relatively low, in Level 2, the neuronal image density is moderate, and in Level 3, the neuronal image density is higher. A sample download.
Download the all of HiNeuron dataset

The HiNeuron dataset is free for academic use only. Anyone can download the dataset through the application.

Cite
When using the dataset, please cite the HiNeuron: Please mention the HiNeuron in the article and refer to the website. For example, "The HiNeuron dataset was accessed from http://atlas.brainsmatics.org/HiNeuron."
The HiNeuron dataset has different specimens, neurons, and levels.
If the dataset is used, such as Specimen1-Neuron1-Level1, please cite "HiNeuron.1.1.1".
If the specimen is used, such as using Specimen1, please cite "HiNeuron.1".
Please cite explanations based on actual usage data.

For citation of papers, we will supplement the citation of the article after its publication.

Licenses
This dataset is licensed under the Creative Commons Zero (CC0).
If you would like to use the dataset for commercial purposes, please contact us.

News and updates
January 22, 2024: The HiNeuron dataset releases 2,809 image blocks.

The project provides the script for improving the spatial resolution of neuron images to achieve three-dimensional isotropy. The image voxel resolution in the three directions of the xyz is different, and the voxel resolution in the z-axis is lower. Therefore, a high voxel resolution image in the z-axis is obtained through neural network prediction.

AINet
The code is written in Python, and the experiment is based on the HiNeuron dataset. This program is used for axial interpolation of neuron images to obtain isotropic images.
Environment requirements

The original code for the HiNeuron has been deposited in github under https://github.com/Brainsmatics/HiNeuron and is publicly available as of the date of publication.