yangzekang2000/CORAL

CORAL is the dataset for CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy.

The benchmark contains data for two neuron reconstruction settings:

• Block-level reconstruction: reconstruct local neuron morphology from small 3D image blocks.
• Brain-wide reconstruction: reconstruct complete neurons across whole-mouse-brain 2D light microscopy slices.

Download

Install the Hugging Face Hub client:

pip install -U huggingface_hub

Download the complete dataset:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="yangzekang2000/CORAL",
    repo_type="dataset",
)
print(local_dir)

Download only the block-level reconstruction data:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="yangzekang2000/CORAL",
    repo_type="dataset",
    allow_patterns=[
        "cubes1937/**",
    ],
)

Download only the brain-wide reconstruction data:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="yangzekang2000/CORAL",
    repo_type="dataset",
    allow_patterns=[
        "slices/**",
        "swcs/**",
    ],
)

Download selected metadata files:

from huggingface_hub import hf_hub_download

annos_path = hf_hub_download(
    repo_id="yangzekang2000/CORAL",
    repo_type="dataset",
    filename="cubes1937/annos.json",
)

hemisphere_path = hf_hub_download(
    repo_id="yangzekang2000/CORAL",
    repo_type="dataset",
    filename="swcs/brain_hemisphere.json",
)

Repository Structure

CORAL/
├── cubes1937/
│   ├── cubes/                 # 1937 raw 3D image blocks (.tif)
│   ├── swcs/                  # 1937 block-level SWC
│   ├── mask-r1/               # 1937 block-level mask
│   ├── annos.json             # Per-block metadata
│   └── C2-cubes1937_tvt/      # Train/validation/test split
├── slices/                    # Whole-brain 2D slice images (.tif)
├── swcs/
│   ├── single-neurons-step2/               # Single-neuron SWC annotations
│   ├── single-neurons-step2-somatree100/   # The initial tree near the neuron soma.
│   ├── all-neurons-step2.swc               # Combined SWC annotation for all 32 neurons
│   └── brain_hemisphere.json               # Hemisphere plane and neuron hemisphere labels

Block-level Reconstruction: cubes1937

cubes1937 provides the block-level reconstruction dataset. It contains 1937 local 3D image blocks, paired with SWC morphology annotations and mask annotations.

Each block is named by its spatial location in the full brain volume:

cube300_x{X}_y{Y}_z{Z}.tif
cube300_x{X}_y{Y}_z{Z}.swc
cube300_x{X}_y{Y}_z{Z}_mask.tif

Main contents:

• cubes1937/cubes/: raw 3D image blocks in TIFF format.
• cubes1937/swcs/: block-level neuron morphology annotations in SWC format.
• cubes1937/mask-r1/: block-level mask annotations in TIFF format.
• cubes1937/annos.json: detailed metadata for the 1937 blocks, including coordinates, SWC filename, intensity statistics, neuron IDs, neuron length, node/edge counts, fiber and branch point counts, hemisphere label, and density.
• cubes1937/C2-cubes1937_tvt/: train/validation/test split files:
  • train.txt
  • val.txt
  • test.txt

Split statistics:

Split	Number of samples
Train	767
Validation	140
Test	1022
unused	8
Total	1937

Each line in a split file contains comma-separated relative paths:

cubes/<name>.tif,swcs/<name>.swc,mask-r1/<name>_mask.tif

Example loading code:

import json
from pathlib import Path

root = Path("path/to/CORAL/cubes1937")

with open(root / "annos.json", "r") as f:
    annos = json.load(f)

with open(root / "C2-cubes1937_tvt" / "train.txt", "r") as f:
    train_samples = [line.strip().split(",") for line in f if line.strip()]

cube_rel, swc_rel, mask_rel = train_samples[0]
cube_path = root / cube_rel
swc_path = root / swc_rel
mask_path = root / mask_rel

Brain-wide Reconstruction: slices and swcs

The brain-wide reconstruction data includes whole-mouse-brain 2D slice images and neuron-level SWC annotations.

Main contents:

• slices/slices/: 2D slice images of the mouse whole brain in TIFF format.
• swcs/single-neurons-step2/: SWC annotations for each individual neuron.
• swcs/all-neurons-step2.swc: a merged SWC annotation containing all 32 neurons.
• swcs/brain_hemisphere.json: an arbitrary 3D plane used to divide the left and right hemispheres, together with hemisphere annotations for the 32 neurons.

brain_hemisphere.json contains:

• plane: plane type, azimuth/elevation, normal vector, offset, and a point on the plane.
• neurons: per-neuron soma location, signed distance to the plane, left/right neurite length, total length, and hemisphere label.

Hemisphere statistics:

Neuron hemisphere category	Number of neurons
Entirely in the left hemisphere	12
Entirely in the right hemisphere	14
Crossing both hemispheres	6
Total	32

Example loading code:

import json
from pathlib import Path

root = Path("path/to/CORAL")

slice_dir = root / "slices" / "slices"
single_neuron_dir = root / "swcs" / "single-neurons-step2"
all_neurons_swc = root / "swcs" / "all-neurons-step2.swc"

slice_paths = sorted(slice_dir.glob("*.tif"))
single_neuron_swcs = sorted(single_neuron_dir.glob("*.swc"))

with open(root / "swcs" / "brain_hemisphere.json", "r") as f:
    hemisphere_info = json.load(f)